PyTorch1Basics.ipynb

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      "cell_type": "markdown",
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        "<a href=\"https://colab.research.google.com/github/mazarrazi/notes_code_log/blob/main/PyTorch1Basics.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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          "data": {
            "text/plain": [
              "'2.1.0+cu121'"
            ],
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          "metadata": {},
          "execution_count": 1
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      "source": [
        "import torch\n",
        "torch.__version__\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **NOTE** :\n",
        "        Scalar, Vector, Matrix and Tensor, all are called Tensor in PyTorch\n",
        "\n",
        "\n",
        "- Brackets \"[ ]\" are rank\n",
        "  - Scalar: 0-dimensional tensor, has a rank of 0.\n",
        "    - (no.) - scalar\n",
        "  - Vector: 1-dimensional tensor, rank of 1.\n",
        "    - [no.] - vector\n",
        "  - Matrix: 2-dimensional tensor, rank of 2.\n",
        "    - [[no.]] - matrix\n",
        "  - Tensor: 3-dimensional tensor, rank of 3. (Also, in PyTorch its a General term for arrays with any number of dimensions.)\n",
        "    - [[[no.]]] - tensor\n"
      ],
      "metadata": {
        "id": "5tPLPpYyGtE-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ***Scalar***\n"
      ],
      "metadata": {
        "id": "4wOj1klzGGY-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Scaler\n",
        "scalar = torch.tensor(6)\n",
        "scalar"
      ],
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          "base_uri": "https://localhost:8080/"
        },
        "id": "bVJMWGVvFEHz",
        "outputId": "1e279f15-29f5-4d74-f64f-433e0f53434f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(6)"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scalar.ndim"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "id": "SOtZI308Fdsr",
        "outputId": "a475a722-e57d-475e-e48b-4bcd50588390"
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      "execution_count": null,
      "outputs": [
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          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scalar.shape"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "id": "dxiUjwyCHm9I",
        "outputId": "63a120fd-1722-4153-9b1c-718bbc130e75"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([])"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scalar.item()  # (only works with one-element tensors)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M8OUUwetFu4b",
        "outputId": "9fb1f575-96ea-4fe8-a838-0b43220a557a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ***Vector***"
      ],
      "metadata": {
        "id": "m_cgWsYyGjF8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "vector = torch.tensor([18,86])\n",
        "vector\n"
      ],
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        },
        "id": "Yiwhrk_sF6jS",
        "outputId": "33e9e3b2-fd05-487f-ed4f-5ee6ee94348b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([18, 86])"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vector.ndim  # counts the brackets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qOXYRKByHV6q",
        "outputId": "0de56d48-746f-4fea-9842-ac84e8386d17"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vector.shape  # two elements inside 1 dimension"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bMSE5cZBHhXq",
        "outputId": "2205077e-c4b7-4a52-e53d-e3b1eb37bd8d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([2])"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# vector.item()  # won't work, a tensor cannot be conversted into a Scalar"
      ],
      "metadata": {
        "id": "qGnl4T5tHwSJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jXKBlBP1H_nZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ***Matrix***"
      ],
      "metadata": {
        "id": "rtOL6QNSINJZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "MATRIX = torch.tensor([[1,2,3],\n",
        "                       [4,5,6],\n",
        "                       [7,8,9]])\n",
        "MATRIX"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "nUjrxEvHAUl0",
        "outputId": "28de7c1c-b3f1-4e40-de4b-9d0335a241bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[1, 2, 3],\n",
              "        [4, 5, 6],\n",
              "        [7, 8, 9]])"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "MATRIX.ndim  # counts the brackets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FFKOxcQYAhsT",
        "outputId": "c220e61d-7a5e-4680-fef0-3e584341e244"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "MATRIX.shape  # three columns and three rows"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lrOYw5HkAmEH",
        "outputId": "e8dbc373-dfcb-4e25-dc7e-868ba63594f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([3, 3])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hykGoSErArVA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "MATRIX2 = torch.tensor([[1,2,3],\n",
        "                       [3,4,5]])\n",
        "\n",
        "MATRIX2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H6LJUVhXISDf",
        "outputId": "cd526143-e0bd-4fee-fcf4-94edecb76082"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[1, 2, 3],\n",
              "        [3, 4, 5]])"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "MATRIX2.ndim  # counts the brackets"
      ],
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        "id": "Ub6K2hG2Ie0p",
        "outputId": "5d4b9970-8db9-4c3b-f327-15054fdf9417"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "MATRIX2.shape  # two columns and three rows"
      ],
      "metadata": {
        "id": "5DOhOb_hJY75",
        "outputId": "79577399-b4b3-4476-debf-42dfc6bde9c8",
        "colab": {
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        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([2, 3])"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "jBCH3bo8JapR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ***Tensor***\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "l8Ly7uVvBh09"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "TENSOR = torch.tensor([[[1,2,3],\n",
        "                        [4,5,6],\n",
        "                        [7,8,9]]])\n",
        "TENSOR"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rs4UDt6eBpoA",
        "outputId": "5cdc7e34-ca7c-45ee-e0ce-7936b1c9293b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[[1, 2, 3],\n",
              "         [4, 5, 6],\n",
              "         [7, 8, 9]]])"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "TENSOR.ndim"
      ],
      "metadata": {
        "id": "2IKDI6RFCYJh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d0b2955a-da67-492d-d17e-baa8476fecdd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "TENSOR.shape"
      ],
      "metadata": {
        "id": "0AegMgRYCpDh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dd7ae004-bb86-42ff-fd41-a26dd26366da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([1, 3, 3])"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "TENSOR.shape outputs torch.Size([1, 3, 3]).\n",
        "\n",
        "The dimensions go outer to inner.\n",
        "\n",
        "That means there's 1 dimension of 3 by 3.\n",
        "\n",
        "![00-pytorch-different-tensor-dimensions 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RwBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAIHsECMpljztnRQABBBBAwFGBpEIlpdy1j0hCnmRHx804mOdkquycMVROHdmbsZrXCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIxL0AQbm4v0VcIAIIIIAAAsEFSlx0q3fL1SbBO0bZ4+i6X2TfgolRjsLhCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIZK0AQbms9eZsCCCAAAIIOC6QmK+wlL9xoCQkJjk+tu+AnrRTsmPaU5J2/LBvE+8RQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBCIWwGCcnF7a7gwBBBAAAEEQhMoWOMCKdGsY2idHei174fJcnTtTw6MxBAIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAghkjQBBuaxx5iwIIIAAAgjETKD4hTdJoZrNYja+78BH1vwg+3+c6lvNewQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBOJWgKBc3N4aLgwBBBBAwA0Ct912m+TNmzfTVBYuXCh//PFHprpo3pRu1VPyla9hO0SepETpduW5tu12Dcs3/SULVmz2az6+Y63snj3Srz7cijJlyoj6hFvmzZsnS5YsCfewkPvXrFlTrr766pD7p3ecPn26bNy4Mf0t3xFAAAFHBa699lqpVq1aWGOmpKTI6NGjwzqGzggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIICAWwUIyrn1zjIvBBBAAIFsF+jYsaNMmjTJ7zoGDRokTz31lF99pBXlrn1U8hQra3t4gbx5ZM3bvW3b7RrGzflNnhj/tV/zyQO7ZOeM5/3qw60499xzZfHixeEeJvfff7+MGDEi7ONCPeDf//63TJ48OdTup/tdddVVMmvWrNPveYEAAgg4KTBjxgxp3759WEPu3r1bNJRMQQABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQECEox1OAAAIIIIBADATy588vK1askCpVqmQa/eDBg1K1alXZu3dvpvpo3lS4cZAkFihqO4TTQbm0lIOyfdog2/OF2kBQLlQp+iGAAAIiBOV4ChBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACB6AQIykXnx9EIIIAAAggYBR599FEZMmSIX5uuJKcryjlZKt48RBKS89kO6XRQzpN6TLZNetz2fKE2EJQLVYp+CCCAAEE5ngEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAIFoBgnLRCnI8AggggAACPgJly5aVtWvXSpEiRTK17Nu3z1pN7sCBA5nqo31T8dZhkpCUx3aYSINy785eIgPen+s3rufUSdk2sb9ffbgVkQbl+vTpI6+99lq4pwu5f6Rbr7Zt21a+/PLLkM9Dx9wlULx4cWuFyZIlS8qhQ4esVSV1ZUn9XKDkDAH9TK9QoYKUK1dOEhMTZf/+/db90+1Njx49GvNJsKJczIk5AQIIIIAAAggggAACCCCAAAIIIIAAAggggAACCLhcgKCcy28w00MAAQQQyHqB0aNHy9133+134ieeeEIGDx7sVx9tRaVOL3o3U0+0HSZQUO6HFVtk6YadxmPHf7VUNu7a79/mSZOtEx7yrw+zJlBQbuzYsVYIxXfIU6dOydChQx3dutb3HIGCci+99JJvd+u9Bp90BcETJ04Y252oLFq0qDRr1kwSEvSfb5nL8ePHZe5c/1Bj5l68ywqBPHnySJs2beTyyy+3grG61XKVKlVEg3Kmsm3bNvn+++/l22+/lUmTJomGrijZJ6A/X3Xq1JEmTZrIeeedJ/o5deaZZ0r58uWlYMGCxgvzeDyyfPly+eGHH2TOnDkyffr0mHwW3HHHHVKvXj3jNdSqVUvat2/v16bPU5kyZfzqqUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEcqMAQbnceNeZMwIIIIBAzAQaNGggixcvlqSkpEzn2LNnjxWa0UCV06VSJ294yxvusCuBgnJXD5oov603B+XsxhNvKGTrhH62zaE2BArKaajI6ZX3Qr0uu6Dcrl27rJWkQh3HqX4acrn//vulV69etmGrnTt3WkEep87JOOELNG7cWLp06SK33HKL6KqSkZSUlBSZMGGCFQZdt25dJENwTAQC+nl93333ySWXXGJ9lS5dOoJR/jlEfx7feustGTZsmLV64D8tsXt14403ytSpU/1OQFDOj4QKBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQyMUCBOVy8c1n6jlXQLdt++ijj4wT6Ny5s2zZssXYRiUCCMReQLfebN26td+JHnnkESs04dfgQEWlzi8HHMXxoJz3bFvffzDgOUNpJCgXWElXserXr590797ddiWr9BEIyqVLZO13XWGsd+/eVkDObqWvSK5It/Hs27evjBkzJpLDOSZMgcKFC8ck0LZhwwbRVeCyYrVHgnJh3nS6I4AAAggggAACCCCAAAIIIIAAAggggAACCCCAQK4UICiXK287k87pAhUqVBDdqs1UateuLatXrzY1UYcAAjEWaNiwofz2229+Zzl8+LC12teRI0f82pyoICjnhOI/Y2T3inK6haIGKzX4nJyc/M+FBXhFUC4ATgyb6tata225GatTTJw40XoO0tLSYnUKxvUKxCoop7i6Les999wT89AjQTkeZQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAguABBueBG9EAg7gQIysXdLeGCELAERo4cKffee6+fxvjx46Vr165+9U5VEJRzSvLvcbIrKKcr7D322GOigZfExMSwJkVQLiwuxzrHOiinFzp27Fi56667HLtmBvIXiGVQTs+mYblu3bqJ/i6IVSEoFytZxkUAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAE3CRCUc9PdZC65RoCgXK651Uw0Bwlo0EJXeixSpIjfVbdq1UrmzJnjV+9UBUE5pyT/Hierg3IXX3yxFZBr165dxBMhKBcxXVQHhhqU++uvv2TlypWi90m3Va1SpYroCrDlypUL6fwPPvigvPLKKyH1pVP4AqEE5Y4dOyZbt261trfX73ny5LHuY9WqVaVMmTJBT3r8+HGpX7++rF27NmjfSDoQlItEjWMQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgtwkQlMttd5z5ukKAoJwrbiOTcJlAjx49ZNSoUX6z0vDcmWeeKbHcOpGgnB97VBVZGZTr2LGjTJo0Karr1YMJykVNGNEAgYJy8+bNk48//ljmzp0ry5Yts1YV8z2JBqyefPJJ6dmzZ8BVBA8ePCi6Ja/eZ4rzAqag3NKlS+Wzzz6TmTNnyqpVq2TPnj22J9aw3MCBA6VTp06SlJRk20/Huvrqq23bo2kgKBeNHscigAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBAbhEgKJdb7jTzdJUAQTlX3U4m4xKBJUuWyDnnnOM3mxdffFEefvhhv3onKwjKOakpkpVBOd2O8d133w1pAgsXLpSmTZsa+xKUM7LEvNI3KLdv3z7rfo4ZM8YKV4V6AY0bN5Zx48ZJgwYNbA957bXXpE+fPrbtNEQukJycLIsWLbJWBdVwnH5t3Lgx7AHr1asnn376qdSoUcP2WF1VToOTTheCck6LMh4CCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgi4UYCgnBvvKnNyvQBBOdffYiaYwwQuvPBC+eGHH4xXreE5XZkoloWgnLO68RaU+/bbb+XZZ5+V2bNnyx9//CFnn32234QJyvmRZElFelAuJSVFhg8fLkOHDpX9+/dHdG4NV+lnRYECBYzHb9++XSpVqmRcmc54AJXZItCkSRNZsGCBaPjOVAYMGGD9PJvaoqkjKBeNHscigAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBAbhEgKJdb7jTzdJUAQTlX3U4m4wIBXQmqa9eufjPR0ItplTm/jlFWEJSLEtDn8HgJys2aNUsGDx4s8+fPP32FBOVOU8TFCw1D3X///TJx4kRrNbJoL6pfv36iq1DaFQ3l/vTTT3bN1MeJwGOPPSbPPfec8Wr057lFixbGtmgqCcpFo8exCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggkFsECMrlljvNPF0lQFDOVbeTyeRwgYSEBNm1a5eULl3abyZPP/20DBw40K/e6QqCcs6KZmdQzuPxyCeffGIF5H799Ve/iRGU8yNxVUVSUpJs3rxZ9Pe8qdxzzz0yevRoUxN1cSRQs2ZNWb16tfGKNmzYIFWrVjW2RVNJUC4aPY5FAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACB3CJAUC633Gnm6SqBeA/KlShRQtq2bSvnnXeelC1bVsqVK2eFiE6cOGEFinSLQP0D8ueffy4rV67Mlntz2WWXySWXXCJnnXWWnHnmmVKmTBk5ePCg6LWtXbvWurYff/xR0tLSYnp9utWe/kG9cuXK1leVKlWsa8qXL59lpVvt7dixQ/T7tm3b5LvvvpO//vorptekgzds2FCuuuoq64/5eg/169ChQ5I3b17rujQYtmjRIvnvf/9rvY/5BYVxAt22sH379parhhH0S5/JrVu3yvr162XdunWyfPly+eqrrxzZwlC3wdTwkqm0a9fOMjK1OVlHUM5JTZHsCMqdOnVKJk2aJEOGDJFly5bZToignC2Naxp0JcE2bdoY5zNo0CB56qmnjG1Uxo9AYmKi9TuzYMGCfhd17Ngx2+11/TqHUUFQLgwsuiKAAAIIIIAAAggggAACCCCAAAIIIIAAAggggECuFSAol2tvPROPV4HWrVvLzJkzg16erjpjKhq2CKekpqY68gdbXVXr1ltvlR49eshFF10kdtfne20aSnv11Vdl1KhREu61ZxxLV9i58847M1ZZrz/44IPTW2JqyKtXr16iK/LUqlXLr69vhYaqdEs9DfQ5XZo3b26tNNaqVauwhtbgngb4ZsyYYX2tWLEirOMDdS5SpIg88sgj0qVLFys8GKhvepuufvXzzz/LSy+9JFOnTk2vdvS7BgVNq7Xpdep2h+lFw456f/XL1D+9X/p3DWkOGzZMJkyYIPpzEGnp2bOnvPHGG8bDS5UqJXv37jW2OVlJUM5JzawNymlI9sEHH5QRI0ZYIc5gMyEoF0wo57e//PLL0rdvX+NERo4caX3GGRupjCsB/R1Tu3Ztv2vS35t58uRxPIhPUM6PmgoEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAwE+AoJwfCRUIZK+AriKjq8lkVTl58qQkJydHdbqWLVtaQSldQS7SoisoaYBt/vz5EQ3x9ttvG4NyGoLq3LmztardtGnT5OKLLw57/OHDh8sDDzwQ9nGmAzQgp6sBXXHFFabmsOumTJkiHTt2DPs43wO6du0qzz//vJQvX963KeT33377rfTu3VuWLl0a8jGhdNQV9EzBt06dOokGIbV06NDBCryZVu8Jdo4tW7ZYq87pz0IkRe/BTTfd5HfomjVrQgpk+h0YQQVBuQjQAhySlSvKBbgMYxNBOSOLqyo1oK0BclN5/fXXrc9ZUxt18SOg/7OArlJr+p20adMmawVZp6+WoJzTooyHAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACbhQgKOfGu8qccrRATgvKPfbYY/Lcc885Yq5bs958880yffr0sMcLFJR79tlnZfbs2SGvkmY6uY4xYMAAU1PIdXpvv/jii5BX2wtlYN0+9Morrwylq20fDcjpSnJOlMOHD8s111wj33zzjRPDWWMEC8p169ZN9P6Huoqh6cJ0q1t9/iIpuuKdbi/sW9JDmr71sXhPUM5ZVYJyznoyWngCAwcOFN1i1VQeffRRGTp0qKmJujgSqF+/vvz+++/GK9J/j+jqwU4XgnJOizIeAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIuFGAoJwb7ypzytECOSUol5iYKLrS2n333eeot26/qiuFTZo0Kaxx7YJykydPlho1akg0q93pheiWpzrGkiVLwrqu9M76R/Pvv/9eihYtml7lyPdog3K6apGuXuRkSUlJkWuvvVbmzJnjyLCBgnK6ne7YsWNFt/6NpkQalKtTp47YbX+rPxt2W7JGc62mYwnKmVQiryMoF7kdR0YvMH78eGslVNNIusX5hx9+aGqiLo4EXnzxRenXr5/ximK1KiBBOSM3lQgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBAJgGCcpk4eINA9gtkdVBu8eLF0rhx47AnPmzYMHn44YeDHqfbWa5atUrWrVtnbet59tlnS6FChQIed/ToUSuUtnLlyoD9MjbaBeUy9vF9refR66pQoYJxa0/f/l9//XVEW6ZqqHD16tVSvXp13yEzvddr+fPPP0VXKDt06JBUrFjRCvnpcfnz58/UN/1NNEE5uzBQ+tjp3/U+6PXranFlypSxnpdSpUqlNxu/79mzR+rWrSsacou22AXl+vTpY21jW6JEiWhPIZEG5bp37y5jxowxnr9p06byyy+/GNucriQo56yo3c/Grl27jKsHOnv2wKOx9Wpgn5zeqluhb9++Xew+Y/V36PLly3P6NF19/XafH+mTvuyyy2TevHnpbx37TlDOMUoGQgABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAARcLEJRz8c1lajlXINjqWBrs2rp1q3GCusKVhppCLR6PJ9Sup/vplmGzZs0KuIrX559/Lrp93LJly+T48eOnj9XQWNWqVa2QXY8ePU7X+77QAN+FF14Y8naYoQblDh48KLp13XfffWetBKYr2GnRUFrHjh2tLe00qGBXqlSpIhs3brRrNta3aNFCvv32W2Ob+r/77rtW2Oqnn34y9tHn4YwzzpBmzZpZK/jpeOkl0qBcyZIlrbCFacvQ9LGnTJkigwcPlqVLl6ZXWd/Vp23btvLCCy9I7dq1M7VlfKOrHunqR9EWu6CchpbKli3rN7zen08//dR6RjV8qKE9na9eq/586Op+GmQoUKDA6WMjDcqpz+OPP356nIwvNBCqYcysKATlnFW2C7oQlHPWmdH8Bdq1a2dt0e3fIqIhyQYNGpiaqIsDAf33jYbV3nnnHdv/IeCzzz6zVlyNxeUSlIuFKmMigAACCCCAAAIIIIAAAggggAACCCCAAAIIIICAGwU0JcMXBjwDOegZ8AblvPkqc6lVq1ZM72Xp0qU93hXPzCf31u7fv9/TrVu3kK7h+uuv9+zevdt2LO/WlSGNo59h3qCc7TjpDd4AoccblAo4pjeM5tm2bVv6IX7fvavoBTze9Hnq3aLWb5z0ioceeijs8bwrAHree+89jzeA6PH+0T3s4/UaR4wYkX4Jft+928x67rjjjqDjeoNmHu/Wtn7HZ6xo2bJl0HFMZhnrvEG5jEPavvYG4jy9e/f2eIN8Qc/pDR5ahjpXLd4tXIMek/Ga0l97V5MzXo93+9mIxksfN9zv3qCcJ9BXjTvt73e7gR8EPNZu3HCv0dT/3HPPNfppZbFixbLUMOP1eYNyxuvauXNntl1T+vV5w1LGa9PP5fQ+fM+Z/671hqI9CxYsMN5frfSGcrnHcfhvtSJFini8oXCP938MsL132pCamhr03yDR/Ox6g3LG8+vv0GjG5dic+XnCfeO+8QzwDPAM8AzwDPAM8AzwDPAM8AzwDPAM8AzwDPAM8AzwDPAM8AzwDPAMmJ8BVpTzPhkUBHKagK4o5w1zGS9bV80KZ0U54yABKp999ln5z3/+Y+yhK3/pdpPhrLjmDfbJb7/9ZtxadNOmTdbWo94/LhvPl7Ey2IpyS5YsEd3uzBvky3iY8XXnzp1l/PjxxraZM2fK1VdfbWyzq9R5nHnmmX7NkYyVcRBdDU5XsNFt+sIpupKaHuMNIhkP0xXShgwZYmzzrdSV2HRVu+bNm/s2We+dWFXObkW5jCdU41atWoX97Ou2w95gpzzwwAPiDc1lHDKk15988olcd911fn3157NSpUp+9bGqYEU5Z2VZUc5ZT0YLTaBLly7iDUEbOx85ckRq1qwZ9ue9cTAqwxLQldo6dOiQ6ZiCBQtK5cqVrS9dsTRY0dVs9XPlyy+/DNY14nZWlIuYjgMRQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgFwkQlMtFN5upukcgu4Jy+odhDSSVKlXKiHnttdeKbisWbtHgnQbwTEVDaxMmTDA1ZaoLFpTTMNOMGTMyHWP3xru6mBX2K1++vF8Xnb/+cTzUkpSUJBr0M22n279/f2v70lDHcqrfbbfdZmuqocVGjRqJd1makE+nYcfly5eLztW3HDt2TPR5DSWg6Hts+vtgQTnv6m1Sr1492bBhQ/ohWfb9+++/l4suusjvfFm9RSJBOb9bEFUFQbmo+Dg4AoESJUpYn6Om3zs63BNPPGFthR3B0BwSpcDTTz8tAwYMiHgU/Z8H2rdvL7///nvEY4RyIEG5UJTogwACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAArldgKBcbn8CmH+OFMiuoNy9994rI0eONJq9+eab0rNnT2NbsEoNpumKb3Xr1vXrOm3aNLnpppv86n0rAgXlNMRVv379sMJfgf4wXrRoUTl06JDvJRjfa/hh7969xjZdPej99983tsWy8r///a+0bdvWeIpwAoUZBxg3bpx07do1Y9Xp1927dxe9P5GWYEG5F154QTR0mB1FV2/UVZ58yzfffCOXXnqpb3XM3hOUc5aWoJyznowWWECD1J9++qkVpjL1/PPPP60wsAaPKVkvEOjfA4GuZv78+da/mT766CM5ceJEoK6OtBGUc4SRQRBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQMDlAgTlXH6DmZ47BbIrKKdbhrVu3doP9eTJk9Yqc7q1WKSlV69e8vrrr/sdriuRlS5dWk6dOuXXlrEiUFBOt9a0284u4xgZX2s4b8qUKRmrTr/WYNTatWtPvw/0QkOAR48eNa62pn98HzhwYKDDY9KmwT0N8PmWPXv2iG7nGsza9zh9r9vafv3116YmGTt2rNx1113GtlAqAwXlNLBYtWpV0WvPjrJv3z4pXry436mnT58uN9xwg199rCoIyjkrS1DOWU9GCyygQd+hQ4caO+nqnu3atZNZs2YZ26mMvUCkQTn9d42G4UMN1kc7E4Jy0QpyPAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCOQGAYJyueEuM0fXCWRHUE631dRQUJEiRfw8FyxYIM2bN/erD6fi7LPPFt2u0lTOO+88WbRokanpdF2goNwZZ5whW7duPd03lBc6H10NxlR0a1JdAS/UsmrVKtHtSX3L9u3b5dxzz5Vdu3b5NsXsvYbK1q9fbxxf/6Cvq9xFUvLkySMaaDOFxtRKzSItgYJyzzzzjDz55JORDh3VcfozoSFRU3n33XfljjvuMDXFpI6gnLOsBOWc9WQ0e4GLL75Y5s6dK/oZairZFag2XUturYs0KKdeGpLT3wePPfaYFZqPpSFBuVjqMjYCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgi4RYCgnFvuJPPIVQLZEZRr3Lix/Prrr0Zn3WYy48pouo2clvTvdq8zticmJopuCarffcu//vUv+fjjj32rM723C8ppkClfvnySlpaWqX+wN4ECZZdccol89913wYY43f7JJ5+IbmlqKhoy1C1LQ12hzjRGOHVqqdvZmkq0W6TOnDlTrrrqKr+hU1NTpXDhwhFvPRcoKFeyZEkrwOl30iyo0GdV52Z6ZnUFQ13JMKsKQTlnpQnKOevJaGaB6tWri/4OKFu2rLGDruKqq8mF+/vLOBiVEQtEE5RLP+lPP/0k11xzjezevTu9yvHvBOUcJ2VABBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQcKEAQTkX3lSm5H6B7AjK3X333TJ69OhswX3ggQdk+PDhAc9tF5TbuHGjVKlSJeCxpsb8+fNLSkqKqUlatWolc+bMMbaZKgcMGCD6h3a7olud6nhTp06V//3vf7J582a7rlHXP/LII/L8888bxwk3AOg7yMsvvyx9+/b1rbbe64p6a9asMbYFq7QLymm9XcAk2JhOtetqgGXKlPEb7rPPPpNrr73Wrz5WFQTlnJUlKOesJ6P5C+iW4hqS0628TWXDhg3SpEmTbNtW2nRNubVOt1DXr4ylUKFCokHHGjVqWF/nn3++tGnTJmMXv9f6O1DD5OvWrfNrc6KCoJwTioyBAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACbhcgKOf2O8z8XCmQHUG5xx9/XAYPHpwtnhrs0m3LAhW7oJyu/KYBsHCLrhKmATZTCTcop9vVLl++XHQL2FCKbo36888/W1+65axuXXrgwIFQDg3a57nnnrO1LFeuXFTbwPbo0UNGjRplvIamTZvKL7/8YmwLVmkXlFOjCy64INjhMW1ftmyZ1KtXz+8cTmxH7DdogAqCcgFwImgiKBcBGoeELKAhq6+++sr280u3Odftv1esWBHymHTMfoEWLVrICy+8YHtf9Qr193udOnWs1UidvmKCck6LMh4CCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgi4UYCgnBvvKnNyvUB2BOVefPFF6devX7bYDhs2THQltEDFLig3efJkufnmmwMdamxzMiinJ9CtV3UL1kiLhsw++ugj6yvSldn03K+//rr06tXLeBl58uSxDQcaD/CptPsjvXa78sorrWCIzyEhvbULyk2ZMkU6duwY0hix6jRv3jxp2bKl3/ArV66UunXr+tXHqoKgnLOyBOWc9WS0fwSSk5NlxowZ0rZt238qM7w6duyYtWrp/PnzM9TyMicJ3HXXXfLWW2/ZXrL+Dh45cqRte6QNdr+DdbtX08qnkZ6H4xBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBnCxAUC4n3z2uPdcKZEdQ7p133pHbb789W8yjCcpNmDBBOnfuHPZ1BwrKtW7dWmbPnh32mP/5z39k0KBBooG0aIqG5vRe/PHHH2EP8/7770unTp38jtNwRoECBfzqw6nQ4Md///tf4yE33HCDTJ8+3dgWrNIuKBfKcxFs7GjbdbtcDSf4Ft2SVVfoy6pCUM5ZaYJyznoy2t8C+ntFfyfdcsstRhJdxfSmm26K+LPSOCiV2SLw6quvyv3332889/bt261tW+22dzceFEIlQbkQkOiCAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACuV6AoFyufwQAyIkC2RGUswsEqd/ixYsd2xrU9354PB7RbV9//PFH36ZM7+1WlIunoJxesG4VqmG1mjVrZrr+cN/oH9j79OkjOu9wiq5Kp6E133L48GHRLWKjKbpqnF2AUMOKei8iKXZBuZ49e8qbb74ZyZCOHaOrAt17771+4508eVJ05aisKgTlnJUmKOesJ6P9LfDaa6/JfffdZ8vRvXv3sD/TbQejIVsF8ubNK99//700adLEeB29e/e2Vng1NkZYSVAuQjgOQwABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgVwlQFAuV91uJusWgewIyo0bN066du1qJNTr2bFjh7EtqypzSlBOPZKSkkRXpdOV3a6//nopWLBgxEyPPvqoDB06NOTjNax22223Gfvnz59fjh8/bmwLpVK3uP3www+NXfUP+BrSi6TYBeXU74MPPohkSMeOeeqpp+TJJ580jleyZEnZt2+fsc3pSoJyzooSlHPWk9FEBg4caK0oamehgfAhQ4bYNVOfAwX09/yXX35pvPL33ntPunXrZmyLtJKgXKRyHIcAAggggAACCCCAAAIIIIAAAggggAACCCCAAAK5SYCgXG6628zVNQLZEZR7+eWXpW/fvkbDypUry6ZNm4xtWVWZk4JyGU0KFy4sl1xyibXSXNOmTUW/SpcunbFLwNdHjx6VOnXqyObNmwP2S2984403RFdiM5UqVarIxo0bTU0h1enzoc+JqbRq1UrmzJljagpaF89BuVtvvdU2rHf55ZfL3Llzg87PiQ4E5ZxQ/GcMgnL/WPAqegFd/XP48OG2A+nnZr9+/WzbaciZAvq7XH9/mYquNnfxxRebmiKuIygXMR0HIoAAAggggAACCCCAAAIIIIAAAggggAACCCCAQC4SICiXi242U3WPQHYE5Z544gl55plnjIga1Fq1apWxLasqc2pQztcnISFBzjnnHGvFuTZt2kjLli2tFeh8+2V8P2XKFOnYsWPGKtvXumKRrkJnKs2aNQu6xa3puPQ6Xdmuf//+6W8zfT///PNl4cKFmepCfRPPQblKlSrJli1bjFPJyhWiCMoZb0HElQTlIqbjQB+BLl26iK7Iqp/tpvLuu+/KnXfeKbrNOMV9Avo/EZx55pl+E9u1a5eUK1fOrz6aCoJy0ehxLAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCOQWAYJyueVOM09XCWRHUO7ee++VkSNHGh3bt28vn3/+ubEtqyrdEpTz9dIQom7vedNNN9kGLdLS0qRQoUJy7Ngx38P93mtIzm57vw4dOsgnn3zid0yoFePHj5fOnTsbu0cTpoznoJxOdu3atVK9enW/eX/66afW1rp+DTGoICjnLCpBOWc9c+tourX2tGnTbMPOuh21hpxPnTqVW4lcP+/58+dL8+bNjfMsUqSIHD582NgWSSVBuUjUOAYBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQRymwBBudx2x5mvKwSyIyinf+jVP/iaioavdDWx7CxuDcqlm2qIberUqbaBC12FbunSpendbb9rKGPSpEnGdt2S9c033zS2hVI5e/ZsufLKK/26aghEAwEpKSl+baFUxHtQbuzYsXLHHXf4TWXHjh2iP6tZUQjKOatMUM5Zz9w4mm69PHPmTMmXL59x+v/73/9EQ+YnTpwwtlPpDgFdcVRXHjWVMmXKyO7du01NEdURlIuIjYMQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAglwkQlMtlN5zpukOgfPnysn37duNkzjvvPFm0aJGxLZrKvHnzyoEDByR//vx+w0yfPl1uuOEGv/qsrHB7UE4te/fuLSNGjDCyarBHg3TBSs2aNWX16tXGbnq8jhNJKViwoPVMFi1a1O/wZcuWSf369f3qQ62I96Ccbq343nvvGadTuXJl0a33Yl0IyjkrTFAusKcGp/v16xe4k7dVQ0J9+vQJ2s9tHS644AKZM2eOFC5c2Di177//3tpe++jRo8b2rKrU1UVr164d9HSjRo0SDfZRwhMoWbKk7Nmzx/Yg/ffU8ePHbdvDbYinoJxuG3///fcHncKGDRvkwQcfDNqPDggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIICAUwIE5ZySZBwEslBAV+c6ePCg8Yy61Ztu+RiLMm/ePNE/fvoWXRGnYsWKAf8g7HuM0+9zQ1CuRIkSlnFCgn50Zy6hruqnx2rgUZ8h35KamipnnHGG7Nq1y7cp6Pvbb79d3nnnHWO/999/XzRMFmmJ96CchuH0j/2moiv4TZkyxdTkaB1BOUc5rcDo5MmT/QbVn41y5cr51WdlxR9//CFnn3223yl37twpGqLOimIXJPQ994oVK6RevXq+1a5+37BhQ9Hflfp5bSoaZNfV5vRzOLvLggULpFmzZkEvo0ePHjJmzJig/eiQWeDSSy+VuXPnZq78/3exWHE0noJynTp1Ev3dH6z89ttvcu655wbrRjsCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggg4KiAxzsaXxjwDOSgZ8AbdvJ4t7P0mEqvXr1idi+9YSzTKa067+pCMTtvKJ9R3qCc8dq8f6iN6LoSExON42llq1atIhozlHkE6pMnTx6PN8xmvK6+ffuGfE1fffWVcQyt7N+/f8jjZLzWH3/80XbM++67L6Ix08f3BuWMY992221RjZs+vhPfvUE54zW++uqrWXKN3qCcJ9BXjTtHGK9PK9sN/CDgsXbjOuHmDUjYXlexYsWyxM40D28QzHhd3jBatl1T+nV6g3LGa/MGb7Ls2ux8fC9s+fLlWXZN6T7Z+d27YqdH74NdUY/SpUvHjYk3KGd3qZnq77777ri55uy8v+GcW/+dNmvWrEyOGd94g+WOm3qDchlPcfq1/g4N59qd6OsNyp0+f6AXS5YsyfJrc2J+jMF/O+AZ4BngGeAZ4BngGeAZ4BngGeAZ4BngGeAZ4BngGeAZ4BngGeAZ4BngGci5zwArynmfXgoCOVFg9+7dUqpUKb9LHz9+vHTt2tWv3okK3UZMt5EsVKiQ33CHDx+WRo0aydq1a/3asqIiN6wop6uuLF682Mh56623yocffmhs862866675K233vKttt7r/atVq5Z4/7BtbDdV6gpKuiqMqZw8edJapU5Xu4q0xPuKcjqvF154QR566CG/KW7dulXOOussSUtL82tzsoIV5ZzUFFaUC8LJinL+QPpz/t1331k/7/6tIuvXr5cWLVrItm3bTM3ZUseKcn+zFy9eXLz/84EcOnTIsfvw8MMPy7Bhw2zH09XfPvroI9v2SBpYUS4SNY5BAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACB3CjA/8mfg1YS8z6g3C8MrGfg66+/Ni7S4Q0mebxBp5g9JyNG2K9M5Q1xeapUqRL1ub3bf3peeeUVa2WeUJ/5eF1Rrnr16h69Nl05K9S52PV78803jfdcKy+88MKQx/duu+o5cuSI7VjhrACXlJTk+d///mc7lncb4JCvy27eOWFFuRo1ani8YTijwxVXXBG1gZ1Ner3dqm/p9awoF97vTrsV01hR7m9HOx/fH4DcsqKcdztez+rVq32nf/r9li1bPFWrVo3550D650Go31lR7u/nuU+fPh7vVrgeb+DZo//+CNXPrp83EOnxbkl/+v77vtC2okWLRn0e3/Ozolx4n/O+frzHj2eAZ4BngGeAZ4BngGeAZ4BngGeAZ4BngGeAZ4BngGeAZ4BngGeAZ4BnINc8A7lmoo7/QYofEp6d7HwG9A+6duXbb7+N2R/lvavmeLyrrtid2uNdWc6jf3TWrUvD9dGwl25Hlv4H5mPHjoU8RrwG5Ro3bnza6ssvv/S0adPGo+GycG00vGZXdu3aFba3OtsVDVt26NAh6DXqVrDelenshrHq27dvH3ScYBY5ISinc7ALDI4bNy5qg2BG6YE4u+8E5cL7fWUXBCMo97ejnY/vh0FuCMqVKFHC411R03fqp9/rM1OnTp2YfwYE+4wwtROU+/t51n+zpBf998eUKVM8GjorWLBgWPdNt96dOHGix7s6Xfpwxu/6u8J0P6KtIygX3ud8tN4cjzfPAM8AzwDPAM8AzwDPAM8AzwDPAM8AzwDPAM8AzwDPAM8AzwDPAM8Az0DOfAbYetX75FIQyIkCzZs3l/nz59te+vHjx2XMmDGyaNEi2bBhg2zevFm0zlS8q92Yqm3rbrnlFvH+Mdi2XRt0S7f//Oc/snTpUtm7d6+xb/78+cUbJJO2bduKbh3qXX0tUz+9Xu0TSonXrVd1fr/++mumKeg2pNOmTbO+fv75Zzl69Gim9oxvzj//fOnfv794g2viDR9mbDr9euTIkdKrV6/T70N5UbFiRfGGWKRYsWK23b0r2MmQIUOsZ8e300UXXSSvvvqqNG3a1Lfp9HtvMNC6t6crInyRE7Ze1andcMMNxq30dDs/74pTkpKSEqFA8MO8AbmAnQrkzSNr3u5t7HP1oIny2/rwt8bd+v6DxvHCqQy0nbBuh+hd5Smc4Rzr6w2CyeTJk/3G84ZSrXvp1+BAhW6Z3bNnz6AjNWjQQAoUKODXT7c51s/7YOXFF1+UqVOnBusWsN3Ox/egFStWSL169XyrXfX+2WeftX7XBZqU/h50qujv08suu8yR4dh69W9Gb1BOhg8f7meqv5u9/+OBtaW8bp2b/vXnn3+KN1AnlStXFu9KgdZXs2bN5LbbbhNvgNxvnIwV+/btkyZNmlhjZax34jVbrzqhyBgIIIAAAggggAACCCCAAAIIIIAAAggggAACCCDgdgGCcm6/w8zP1QJLliyRc845J6o5argiOTk57DFGjx4td999d0jHaTBMAxPerelEaIsYqQAAQABJREFUz1eqVCnrD8saksmbN6/tGG4NymWcsHr8/vvvVqBQA2HeFfksnzPPPNMKDmooJlDZv3+/1K5dWzTAE27R+6f3MVDxLodjhR5XrVplXVuZMmXkggsukGrVqgU6TLxbu0r9+vWtkGbAjiE05pSgnAYkNm7cKBpC9C0aBP3www99qx17T1DOMUprILsgWCyDco8++qgVTHV2Jv6j9e7dW15//XX/hjBq7Hx8h8gNQbnnn39eHnnkEd+px+y9k88gQbm/b5NdUM7pm+jdnluuuuoq8a4o5/TQ1ngE5WLCyqAIIIAAAggggAACCCCAAAIIIIAAAggggAACCCDgMgGCci67oUwndwnoql7ffPNN0BVMAqlEGpTLly+fjB071lpBJdD40bTlhqBcND4aYuvcubN88MEHEQ2TkJAgEyZMsFbzi2gAm4O8285Jp06dZNKkSTY9wqvOKUE5ndVTTz0lTz75pN8EZ86cKVdffbVfvVMVBOWckvx7HLsgmJMhJd8rzklBuW7dusm7777rOwW/9wTl/EiirnDyGQw1bN+jRw9rhdqoLz5OB8iqoJz+jA8dOjRmCvEUlAsliK8Q3m2LRf+nCQoCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggkFUCBOWySprzIBAjAQ0svPHGG1KwYMGIzhBpUC79ZBoK0nBQLApBOXtVDcnpH6J1y9loim7n+tZbb8kdd9wRzTCnj01NTbWCd7q1rFMlJwXlzjjjDGsVvaSkpEzT15+zs846S7Zv356p3qk3BOWckvx7HIJygT31M+Ouu+4K3Mnbqp9P3bt3D9ovJ3fIqSvKFS1a1NoW3fezyvde6O8aXblWVz51a4l1UE4//5977jkZOHBgTAnjKSj33nvvSZcuXYLON5Kt44MOSgcEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAIIAAQbkAODQhkFMEqlevLqNGjZIrr7wy7EuONiinJ2zVqpX1R+AmTZqEfX7TARq2+uqrr+Sjjz4KOQimgYw777zTbzhdMU1XXQu3aIBMV0YzldatW8vs2bNNTX51ui3q/PnzpXTp0n5tkVZ89tlnVjjx119/jXSITMfpynIPPfSQDBgwQIoUKZKpLZw3unpUz549Zd68eeEcFrRvTgrK6WR0pUVT8HDEiBFy//33B51vJB0IykWiZn8MQTl7G21Zu3attTV0oF4nTpyQmjVryqZNmwJ1y/FtOTUod80114j+LglWdGXQW265JVi3HN3erl070c9n/beU0+WPP/6Q22+/XX755Renh/YbL56Ccvpzr1vIByrHjh2zzLdt2xaoG20IIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAgKMCBOUc5WQwBLJXoFKlSqIhLv3SP/iWLFnS+ipWrJho8MtUtm7dKroKlhPl+uuvl759+0qzZs0kOTk5rCGPHDkis2bNkunTp8vnn38uBw4cCOv4wYMHy+OPP+53zEsvvWSFwPwaglRoeGznzp1SpkwZv566us7SpUv96u0qdCzdWkyDjBoqvPjii6VAgQJ23W3rZ8yYYQXkFi1aZNsnmoZy5crJ008/LTfffLPoakOhlj///FNefvllK6ypwUuniwYNzj77bL9h1XLOnDl+9dldUaFCBVmzZo0UKlQo06XEMhRAUC4TddRvsiMo17t3byusE/XFBxlA5zZ16tQgveybNfwSSvjt9ddfF52T24v+ztPPv6wqurJbw4YNoz6d/m588MEHA46jYXH97F21alXAfm5prFy5slxxxRXW1+WXXy7ly5ePaGrqpv+O0dXSNFSvq/JlRYmXoJz++1PDtMGK/tz069cvWDfaEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAFHBQjKOcrJYAggoAK6KlnLli3lsssus0J4upqaBs40XFGlShXZvHmz9aXv9fXGjRvlp59+kpSUlFwBmC9fPmnevLnUrVtXNFRVsWLF019qonUbNmzI9LV8+XJZt25dlvhoyLFFixbStm1bqVatmpQtW9b60j98a6hy165doqu8LV68WL744gvRleQomQXstiTWbZLvu+++zJ0deBdNUG7F5t1y5NgJ41XMXLhWxswyr1y49f3AIRvjgD6VGiDV58hUfv75Z7ELXr7yyivi5Pa+vue3C8rpapcLFy707X76vQaP9LPMzUW3U9RtFQMV/SzXsEysthoOdG7aQhPQwHWjRo0Cdtb7rNu759aiIUENJWqIXENz+j39a9++fdZr3SJ+//79smfPHlmyZIn186+rxx08eDAmbEOHDrXC9qbBS5UqJbqKrW/ZvXu3MfTv28+p97ots27PHKjo/xxRtWpV698SgfrRhgACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgg4LUBQzmlRxkMAAQQQyPUCBQsWlNWrV4uu8pixaKhCt6PUgKiTJZqgXKDrGDfnN3li/NfGLrEOyhlP+v+VuoWtbpUYq2IXlAt2vquuuspaGTNYv5zcPm7cOOnatWvAKUS6kmfAQWl0TEBXm9Wws91Ks3oiDYVq6EpXDKXEj4CuLNu+ffuwLiirg3ITJ04Mul3vkCFDjKsAhzUxOiOAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACEQgQlIsAjUMQQAABBBAIJqBhIg0V+ZZRo0bJvffe61sd1XuCclHx+R1MUM6P5HSFrgB61llnnX7v++Lw4cPWSlEazqHEp0CHDh3k448/Dnhxo0ePlnvuuSdgHxqzXiAnBOW2bdtmrYxrp3PgwAHrM0JX5aMggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAlktQFAuq8U5HwIIIIBArhBISEgQ3YKvcePGmeZ74sQJqVWrlrXlcKaGKN4QlIsCz3AoQTkDirdKt2IOtgX04MGD5YknnjAPQG1cCOhqjL1797a9Fl35skaNGrJlyxbbPjRkj0C8B+Xq1KkTdDv2gQMHytNPP509gJwVAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEcr0AQblc/wgAgAACCCAQK4FLL71U5s6d6ze8BlV0+1CnSrCgXP68eWTNW73Fm90Lq8R669WGDRvKb7/9FtY1aedYb7164403ytSpU8O+LrdvvXrnnXfK22+/beuyf/9+a6Uo/U6JX4Hff/9d6tevb3uBw4cPlwceeMC2nYbsE3j33XelW7duYV1AVm69qquljhw50vb69uzZY31GHDp0yLYPDQgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBALAUIysVSl7ERQAABBHK9gK4olzdv3kwOGhZYs2ZNprpo3lTq9JIES8FVKVdcShYpENZp/tp/RDbvPuh/jMcjWyf086+PoKZBgwZSqFChsI7csGGD7NixI6xjwumsqwHqfUtOTg7nMGslJd1W0K1lwoQJctttt9lOb8CAAfLss8/attOQ/QJlypSRnTt3ej8uzKnZo0ePWisHah9K/AnoZ6V+ZoZTUlNT5ddffw3nkIj7TpkyRW666Sbb4x955BEZNmyYbTsNCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggECsBQjKxVqY8RFAAAEEEIixQKVOL3qDcokxPkuG4T1p3qDcQxkqeJkbBLZu3SoVK1Y0TlVXrdKtWVkpysgTN5UaYtIwk13REJOGmSgIhCug4UsNWGoY01S0TT8jNIxJQQABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQSyS4CgXHbJc14EEEAAAQQcEqh46zBJSMrj0GjBh/GcOinbJvYP3pEerhGoXbu2rFy50nY+/fv3lxdeeMG2nYb4EHjzzTflnnvuMV7MwYMHrSCTrnhJQSBcAV3pbunSpbaH6Xa+uq0vBQEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBDITgGCctmpz7kRQAABBBBwQKDizUMkITmfAyOFNoQn9Zhsm/R4aJ3p5QoBDVdpyMpUdBvc6tWrs1KUCSfO6jTsqKFHU3n66adl4MCBpibqEAgq0KdPH9sg3JYtW6RGjRpy/PjxoOPQAQEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBCIpQBBuVjqMjYCCCCAAAJZIFDhxkGSWKBoFpzp71OkpRyU7dMGZdn5OFH2C7z99tty5513Gi9EAzKvvfaasY3K+BEoVqyY7N27VxIT/bdp3rdvn1StWlUOHDgQPxfMleQogfHjx0vnzp2N16xB29GjRxvbqEQAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEslKAoFxWanMuBBBAAAEEYiBQ7tpHJU+xsjEY2TzkyQO7ZOeM582N1LpSoGzZstKsWTPxeDynv9LS0qzXX3/9NStF5ZC7rvdQ76Xex/T7p6+3bdsmS5YsySGz4DLjUaB8+fJywQUXnP58yPiMzZkzR1JTU+PxsrkmBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQyGUCBOVy2Q1nuggggAAC7hMo3aqn5CtfI8smdnzHGtk927wNZ5ZdBCdCAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgDAGCcmFg0RUBBBBAAIF4FCh+4U1SqGazLLu0I2t+kP0/Ts2y83EiBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEohUgKBetIMcjgAACCCCQzQIFa1wgJZp1zLKr2PfDZDm69qcsOx8nQgABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAIFoBgnLRCnI8AggggAAC2SyQmK+wlL9xoCQkJsX8Sjxpp2THtKck7fjhmJ+LEyCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIOCUAEE5pyQZBwEEEEAAgWwUKHHRrVKwepOYX8HRdb/IvgUTY34eToAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggICTAgTlnNRkLAQQQAABBLJJIKlQSSl37SOSkCc5ZlfgOZkqO2cMlVNH9sbsHAyMAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBALAQIysVClTERQAABBBDIBoFCdVpI8aYdYnbm/Quny5GV38VsfAZGAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgVgIE5WIly7gIIIAAAghkg0CJi27xbsHa1PEzH1230Lvl6oeOj8uACCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIZIUAQbmsUOYcCCCAAAIIZJVAQqKUaNbR0bCcFZL7YbKIJy2rZsF5EEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQcFSAoJyjnAyGAAIIIIBAfAjoNqzFGl0jCXmSI74gz8lUObD485hst1q9enVp37592Nc2Y8YMWb9+fdjHcQACCCAQisA111wjNWrUCKXr6T4nTpyQkSNHnn7PCwQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgfgUICgXn/eFq0IAAQQQQCBqgaRCJaXoOW2lQNVGkpCYFPJ4nrRTkvLnYjn42yw5dWRvyMeF0/H666+X6dOnh3OI1fe6664TDctREEAAgVgITJs2Tf71r3+FNfTBgwelWLFiYR1DZwQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgawXICiX9eacEQEEEEAAgSwVSMxfRAqc1VDylj5LkouVl6RCxSQhuYAkJOURz6mT4klN8QbiDkjqgR1yYvcmSdm0VNKOHYrpNRKUiykvgyOAQIQCBOUihOMwBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQCAHCBCUywE3iUtEAAEEEEDAbQIE5dx2R5kPAu4QICjnjvvILBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABkwBBOZMKdQgggAACCCAQU4FIg3LXXnutfPbZZ45fW/HixaV8+fJSrlw58Xg8sn//ftm3b5/89ddfcuzYMcfPx4CxF0hMTJQyZcpIhQoVpGTJkqLbY27dulV27twpaWlpsb8AzhCRQN68eaV06dLWl9631NRU2b59u+zYsUOOHj0a0ZjhHERQLhwt+iKAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACOUuAoFzOul9cLQIIIIAAAq4QCBSUe+mll4xzPHLkiAwZMiTi4JoGpxo0aCDnnXee9XXOOedIpUqVrIBc/vz5jec8deqULF26VH788UeZNWuWfPHFF6J1lPgTSE5OliuvvFJuvPFGad26tRWQS0pK8rvQkydPWqGrdevWycyZM+WTTz6R1atX+/WjIusEzj//fGnfvr20atVKmjRpIqb7pldz4MAB+fbbb2XKlCny6aefyqFDzm8R3aVLF2nYsKFx8tWrVxf97PItGsIsVqyYbzXvEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEE4kyAoFyc3RAuBwEEEEAAgdwgYBeU05XcSpQo4RhB4cKF5d5775WWLVvKxRdfHHWYZdOmTfLGG2/I8OHD5fjx445dJwNFLqAhxwEDBkjPnj1FVwaMpKxcuVLGjRsnQ4cOjeRwjolQQENxzz77rLRp0ybsEXSlxxEjRsiTTz6ZZT+LuqKlBvR8C0E5XxHeI4AAAggggAACCCCAAAIIIIAAAggggAACCCCAAALxKUBQLj7vC1eFAAIIIICAqwWyKihXq1YtWbVqleOWy5YtE115atGiRY6PzYChC1x22WUyevRoqVmzZugH2fTUsFzdunVtWql2WuCJJ56QZ555Juphly9fLl27dpVffvkl6rGCDUBQLpgQ7QgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBAfAsQlIvv+8PVIYAAAggg4EqBnB6U05uSmpoq//73v62tO115k+J8Ug8//LAMGzbMsaskKOcYZcCBdFvVt956S26//faA/cJp1NXlLr30Uvnpp5/COSzsvgTlwibjAAQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgrgQIysXV7eBiEEAAAQQQyB0CbgjK6Z06ceKEXHfddTJr1qzccePiZJbdu3eXMWPGOHo1BOUc5bQdbODAgTJo0CDb9kgbdu7cKeeff77o9sixKgTlYiXLuAgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBA1ggQlIuhc/XKheXMSgUzneH3FQdkz77jmerCfZOQkCAN6xXLdFhKyilZvf5QpjreIIAAAgggEK8C8RSUO3LkiGzdulW2bNki27dvl/z580uVKlWkatWqUrJkyaCE+/btE93idffu3UH70iF6gRtvvFEmT54siYmJQQfTbXc3btwoGqIqVKiQdU+rVasmxYpl/neUDkRQLihn1B1atGghc+fOFV1Vzq7s379fFixYIEuWLJGlS5dKcnKyNGrUSJo0aWIF4fTn067oMdrv1KlTdl2iqicoFxUfByOAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAAC2S5AUC6Gt+CGdmfIFS3KZzrDyHFrZNmqA5nqwn2TJylBhj97XqbDtmxPkSEjlmWq401wgSKF8kiligWlRLG8UqRwsncbvTTZf+CEFWbcvO2oeDzBx6AHAggggED4AtkVlPN4P9gXLlwon332mbUK3Nq1a0WDOXbl7LPPlmeeeUY6dOhg18Wqf/vtt0VXOaPEVqBMmTKyevVqKV68uO2J/vrrL3nttddk0qRJsmbNGmM/DUBqaOu+++6TK664QvR/QiAoZ6RytFKDbw0aNLAdc/78+XLTTTfJjh07jH3q169vbXVcvXp1Y7tWdurUST744APb9mgaCMpFo8exCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggED2CxCUi+E9ICgXQ9woh65VrYhcfnE5Obt2ce+KNObB9u47IT8u2i1zvtshx4+nmTtRiwACCCAQkUBWBeU0EPXzzz/LsmXLrHDc559/bhvCCTQR3dJRw3Vly5Y1dtMVrMqXL8+qckYd5ypHjRolPXr0sB3w448/lnvuuUc0LBdqqVOnjvTq1cvaRrdfv36hHka/MAUuv/xy+eqrr2yP0nCj+qemptr20YYSJUrIxIkTpW3btsZ+GnjUgGtamvP/diMoZySnEgEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBDIMQIE5WJ4qwjKxRA3wqGTEhPk2jaV5Epd6U+f/hDKX7uPydgP14uuMEdBAAEEEHBGIKuCcs5c7d+jXHXVVfLFF19Yq4+Zxr3zzjvlnXfeMTVR54DAOeecI7/++qvttp2vvPKKPPjggw6ciSFiIfDJJ5/IddddZxz6l19+kaZNmxrbTJV58uSxVgC0W1nu5ptvtrbnNR0bTR1BuWj0OBYBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQyH4BgnIxvAcE5WKIG+HQd9xSTc5rWDLso3VL1pdGrSQsF7YcByCAAAJmgZwYlNOZ6KpXul2nqUyZMkU6duxoaqLOAYGPPvpIbrjhBuNI06ZNk3//+9/eLdPZM90IlM2VGmzTLY4LFSpkvBK9d1OnTjW22VXefvvttsFUDeUF2y7ZbtxA9QTlAunQhgACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgjEvwBBuRjeI4JyMcSNYOgrLykvHa46w3ik/l39aMpJKVQwj7FdK/ftPyFDX18uh46ctO1DAwIIIIBAaAI5NSjXsmVLmTdvnnGSCxYskObNmxvbqIxOoGjRorJr1y7Jly+f30C7d+8WXVns4MGDfm1UxIdA48aNrdUATVezdu1aqV27dthbpWr4btWqVVKtWjW/YQ8dOiSlSpUKuo2r34FBKgjKBQGiGQEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBCIcwGCcjG8QbEKyiV6tw/t9K8qma789xX7ZfEf+zLV8eYfgVIl8smghxpIYuI/ddYrb0Bu9nc7ZM63O+SwNwBXvGiy1KtVTNpeVkFKlfT/Y/yCX3bLBx9t8BmEtwgggAAC4Qrk1KBcyZIlZc+ePcbprl+/3gpsGRupjEqgc+fOMn78eOMYffv2lVdffdXYRmV8CPTq1Utef/1148UMGjRInnrqKWNbsEq99y+//LKx22WXXWYbajUeEEIlQbkQkOiCAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCMSxAEG5GN6cWAXlYnjJrh36lusry8UXlPGb3/vTNsiPv+72qy9dKp88fG9dKVwo8wpzp9I8MuiFP2Tv/uN+x1CBAAIIIBC6QE4NyukMdeWyIkWK+E12x44dUqFCBb96KqIX+OKLL6Rdu3Z+A+3cuVMqV64sx4/ze9kPJ44qhg4dKv379zdeUbdu3eS9994ztgWrbNOmjcyaNcvYTc/56KOPGtsirSQoF6kcxyGAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAAC8SFAUC6G94GgXAxxwxi6SKFkGfxYQ0lK0sf9n7Jo6T4Z++G6fyp8XtWtWVTuu6OWT63INwt2yZTPNvnVU4EAAgggELpATg3K6XaOutWnqfz444/SrFkzUxN1UQjkz5/fCicmJyf7jTJ69Gi55557/OqpiC+BMWPGSPfu3Y0XdcUVV8jXX39tbAtWqVu2rly50tht9uzZ0rp1a2NbpJUE5SKV4zgEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAID4ECMpFeR8SvIIe7/adppLTg3KB5pZxvqH2y3hMVr6+8LzS0vnGKplP6b1nA1/8XXbvtV+B5jbv9rYXNSmd+Tjvu/37T8h/hi71q6cCAQQQQCB0gZwalNPgzZdffmmcqK6KpatjUZwVaNSokSxatMg4aNu2bW3vh/EAKrNFYOrUqXLjjTcaz12rVi1Zs2aNsS1YpYYojx49Kgn6j1Gf8ueff0q1atV8aqN7S1AuOj+ORgABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQSyW4CgXJh3QLfkbHpOKalfp5iULJ5XihROllOnPLLvwAnZviNFfli0W/5YeUDSvFt0RhuUO6NCQSleLG9IV3js2ClZu+FQSH3TO/mGwP7cfMS7dVmaXHheKalXu5hUqVRIjp04JTt2HbNWUVv42x7r0MTEBGlUv4Toimt1axSVokXzypZtR2T9psMy/6e/ZLu3fzyVu26tLo0alMh0Ses3HJaXRptXINGONasWkQe61xbx/7urNc7gV5fJtp0pmcbkDQIIIIBA6AI5NSg3ceJEueWWW4wTffzxx2XIkCHGNiojF+jSpYtxa86UlJT/Y+8+wJyo1gaOv7ssvffei3SkCQpSVLAg+iGKCiICFwsqqKhYKCIiKHBpYkEQQVBUREEv14IdFJQmVUAB6b33lm/eaPZmWjbZTbLJ8j8+PMmcc+bMzG/Orruzb94jefPmlbNnz6Z+cPaMioAuj6rLpDoVvYe6nHFqy4EDByR/fvPPeTrW+fPnJUeOHHLmzJnUDm3bj0A5GwkVCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggEBcCRAoF+TtSkpKkBuuKiGtmheXxMTAO23ZfkImTv9Tml9eWK6+spip86tvb5DV6w6b6tw2unQoL5fVLejWbKrftvOkDB272lQXaEMzb7zyYn1Tl69+2CX1axWQAvmdg/O+/nG3zPlym9zbqZLUMAIFncqx4+dk1ITfvcF1Tu3RrtMEI8MH1JXs2TKZDj1r7lbR63EqScYSrU/3qiHFimRzavbWBdrfdScaEEAAAQSSBeIxUK53794yevTo5Gvwf6NBObVr15Y1a9b4V/M+DALDhw+Xxx9/3DbSsmXLpF69eqb6woULS5cuXaR69epSvnx5KVeunPFzW6J3udy9e/fK1q3G//+//lp0Wc79+//+AIBpADYiIjBr1ixp166d49hly5aVLVtSt6R9pkyZ5NSpU5KUlOQ4dtWqVWXdunWObampJFAuNWrsgwACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgjEjgCBckHcCw2Se/CeylKlYp4gev/dRQPG1v95VOrVNme4iOVAuWAubsPGo1K5Qu6AXQ8fOetd1vTs2QsB+0WjUTPyDXmqtu1Qo95Y55qB74arS0iba0rY9vGvWLhkn7wzc7N/Fe8RQAABBEIQiKdAucyZM0vXrl1l/PjxrgE5b7zxhtx///0hCNA1WAG3bGTTp0+Xu+66yztMxYoVRTP6dezYUXQ5zpTKhQsXZO7cud59Vq5cmVJ32tMoMGnSJOnWrZvjKE2aNJGffvrJsS2lSg2y27x5s2u3Nm3aeO+za4cQGwiUCxGM7ggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBAjAkQKBfEDXFaujOI3Ry7xHugnONFOVS+/f4m+XV5+mdqqWIE9fXWJVT9i0fkseeWyukz9kC+IoWyybO9axiBEPql4V42/mUs3fq6+9Kt7nvSggACCCCgAvEQKJcvXz7p0KGDPP30097MZG537uhRI4i8cmXZvds5U6nbftQHJ7B27VrRzGDW0q9fPxkyZIi0bNlSNGOZ3q9QiwbMTZw4UR5++OGwLtEZ6nlk9P4jR46Uxx57zPEyb7/9dvnggw8c21KqbN68uXz33Xeu3Tp37izTpk1zbQ+1gUC5UMXojwACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgjElgCBcincj3Klc8oTPas59lq7/og3K9nuvaekWOFsUql8bqlaOXDWuVAC5RrXLyTljeNbS2JiglzRsJCpOhxLr+qAmg1u9hfb5OTJ89L0ssKuS6zu239a5n6zw9uvWeMiUq2K+bqtGdfy5M4sg56oZTrncG/ofRg2zrzk3ZWNCssd/1fWdCjN9tf3heWmOt/GI0ZQnX/GvHPnLsjCJfulqTGOfwk0hn8/3iOAAAIIOAvEQqBcjx49pEWLFqYTzJ07t2iWKv2XN6/zMuP+O2hwnAbP/PLLL/7VvA+jwLZt26RkyZK2ETXL39mzZ2Xy5MmiWf/SUr755hvv0qBHjhxJyzDs6yLwxBNPyMsvv+zYqsvqaiBdaorOgbfeest1V83yqNkew1UIlAuXJOMggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAukjQKBcCu73da4ktatbMpQYGcne++Qvmf/LXtve3sCsm43ALJeEZKEEytkG/6ciKVOCjHmhvqk5XIFyGmi2dccJ79h6nCcfrC4li2c3HUuDxwaPWi37Dpz21mfJnCj9H60pBfJnSe63Y9dJGTJmdfJ23jyZ5cWn6yRvR+LNrj2njPNaZRq6beuScl3L4qY6N6vG9QpK59vKm/rO+XK7N3iw863lTPW60bv/Ejl3zpgMFAQQQACBkAViIVBOM4l179495HP37bBq1Spp27ZtwKUffX15Tb3A4cOHJU8ec0C+jta/f3/RrHJZs2ZN/eB+ey5YsEA0Q9n58+f9ankbDgENSP32228dh5o3b560atXKsS2lSs0mp/fMrWgWu1GjRrk1h1xPoFzIZOyAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCMSUAIFyAW5H8aLZpZ+xDKc16O3dWZtlwa/7XPe8okEh6dS+nGN7LAfK7dx9Ul4Y/b/gNr2AG1uVkOuvKmG6ljXrD8v4yRtMdZq1TYMEfeWMsazpowOX+jYlvQLl2rcpLVc1LZp8HvrG6fxz5UzyBvvpq69s33lSXnpljTerngZMWsuTg5fL8RPnrNVsI4AAAggEIRCvgXIej0e+/PJLee211+Szzz4jqCqIe52WLgkJCUZQ+jlJTEwMaRjNNLd69Wr5888/vdkBa9asKdmyZUtxjIEDB8rzzz+fYj86hCaQK1cuOXTokGTKlMlxx3r16smyZcsc29wqU1p2VffzLc/rNkao9QTKhSpGfwQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgtgQIlAtwP7p0KC+X1S1o6rHXWHL0uRErTXVOG5phrVgR+x9kYzlQbumKgzLpvT9Nl6PXrw7+5esfd8usuVv9q+TqK4vKLTeUNtU99txSOX36grcuvQLl7mxX1ruErP+JLVt1UCZON1/n3UYmuUZGRjlfMeIgZMRra2Xz1uNS2VhS95F7L/E1Jb/2e2mFHDx0JnmbNwgggAACwQvEa6DcwYMH5dFHH5X3339fTp06FfwF0zNVAhpgdfTo0aD3XbFihfTs2VMWL15s/Azyd+Zb3VkDtGrUqCHjxo2TZs2auY6nQXmXXnqpN8jOtRMNqRLQ5YkbNmzouO+MGTPkzjvvdGxzq9QMddalk619hw4dKs8884y1OtXbBMqlmo4dEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIGYECBQLsBtGNy3thTI97/lRLXrp8ZSnJ9/uzPAXn83OQWOaUssB8p9t2C3fPiZOQCuauU88nC3Kqbr1SA5DZbzL42MgLq7LQF1A4evTF6eNVvWTLYlUP33D8f73/84IvrPv9xzewVpeGkB/yr5dfkBefv9jcl1VSrmkd7/Ml/jt4bFzH8sShTLLs9qZkFLef7fq2T3XoIkLCxsIoAAAkEJxGugnO/i9u/f780qN2jQIG/GM189r+EVKFq0qOzatSuoQTXYSpfSPXHi7yXknXZKSkqSl19+2Rvs6NSudW+++abce++9bs3Up1LgX//6l9fWaXdd7laD0ObOnevUbKt76qmnRIPgUiqjR48OeK9T2t/aTqCcVYRtBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQCC+BAiUc7lfiYkJMmZwfWOpL3OHYLOI5c6ZWYY+U0cSLPvHcqDc3K93yH/m7TBdcLnSOeWJntVMddM+2iw/LzYvPVu7Wj65727z8qSDR62SXXvSN5CsR6eKcmnN/KbzX7R0v0z9cJO3LikpwRsEV6TQ/7L/HTCyxL1gnPtpY/lYLfmNYMkXjKBJaxk6do1s2+n+x3hrf7YRQAABBP4nEO+Bcr4r+fzzz+W2226TY8eO+ap4DaNA4cKFZc+ePSmOOHbsWOndu3eK/XwdXnjhBXn22Wd9m6ZXDbQrVaqUaPZASvgEsmfPLtu2bZMCBcwfYPAdQZc11sDTwYMHy4ULf/8M5mvzvebMmVPeeust6dChg68q4OuIESPkiSeeCNgnlEYC5ULRoi8CCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggEHsCBMq53BOn4KgTJ87LE4OXuexhr9bgKh3Hv8RyoNzsz7fLl9+bs+WVLJ5DnulV3f8SZPKMjbL4twOmumpV8shDXc1Z2WIhkKzrHRWkQR3zH2RXrD4kb0z7w3v+N7YqIddfVcJ0LeMnb5A16w8n1znNBW3UYLqd6RwImHySvEEAAQTiTCAWAuWyZs0qmTNnNsnlyZNHKlWq5P1XsWJF7zKdTZs2NfWxbixZskTatGkju3ebs61a+7EduoAGRqUUhHj48GEpX758SIFtGqy1ZcsW0fGdSpcuXWTq1KlOTdSlQaBPnz6iwWuBynfffSdz5syR5cuXy2+//eb9Gq1fv740aNBA7rjjDqlWzfwBjkBjDRw4UJ5//vlAXUJqI1AuJC46I4AAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIxJ0CgnMstqVgulzx2X1VTq2ZH0yxpwZYnjUxsZY2MbP4llgPlnJZULV4km/R7tKb/JcjE6X/KslXmLCuXGMuX9rIsXzr81bWyeetx077R3uh0Szm5omEh02H/3HxM/v3G71LMuLZnetWQTJn0y+DvYl2WVWs129zAPmYDre//0ko5cOi0vqUggAACCIQoEAuBcsGesgbBvfTSS1Kjhn0Zbt8YixYtksaNG/s2eQ2TQEJCgmt2Md8hNBBKA6JCLaNGjZJHHnnEcbfXX39dHnjgAcc2KlMvoPfz66+/lpYtW6Z+EMueGqCaJUsWyZ/fnEFYuz322GOi9zlchUC5cEkyDgIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCQPgIEyrm4N6xTUO65o7ypdcPGozL6zXWmukAb999dWWpVy2vqEsuBcjM/2yrfLjBnwylqBIkNsASJTTCysf1mZGXzL1Uq5JbePS7xr5JYCJS77cbS0qJJUdN57dx9UoaMWS2P9KgqlcrnSm47fuKcDP73Kjl6/Fxynb4pZWTVe9qSVU/r+76wXI5Z+mo9BQEEEEAgZYF4CpTTq8mUKZM89dRTokt2upV27drJJ5984tZMfSoFdClUXbbTqRw9elTKlCkjhw6Zfy5x6mutK1u2rGzevNla7d3WbGZ169Z1bKMybQK6rO0PP/zgzQKYtpH+3luXYX3llVekSJEituF69OghEydOtNWntoJAudTKsR8CCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggEBsCBMq53Idrriwm7W4oZWoNNVDurvbl5PIG5mxmBMqZSCO+cVPrknJty+Km4xw5elY+/XK7dDLuj3+Z+sEmWbRsv3+V9321ysayst3My8pqwyMDlsrZsxds/alAAAEEEEhZIN4C5XxX9MEHH8htt93m2zS9rlq1SurUqZNiBjTTTmykKLBv3z4pWLCgY7/58+fLlVde6dgWTKUu26rL7VrL2bNnvVnKrPVsh0egWLFi8p///Efq1auX6gHPnTsnPXv2lDfffFPcgil1qdb3338/1cew7kignFWEbQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgvgQIlHO5X80aF5Hbby5jal259rC8PnWDqS7QRpcO5eWyuuY/7F6sgXKJiQmSJ3fmQFxpbrtwwSMaBOdfWlxRRG5ra76P2n7ixHnJkSNTctffNxyRcW+tT972f9O4fiHpfGs5/yo5dfq89HlumamODQQQQACB4AXiNVBOg6qWLVsmFSpUcLzY9u3by6xZsxzbqEydwF9//eXNGue097Rp06Rz585OTUHVrV69WqpXr+7YN1euXHL8ePouIe94YhmkUn0HDBggDz/8sGTLli2kq9IMgrfffrt8+eWXUqJECdm+fbvj/tddd5188cUXjm2pqSRQLjVq7IMAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIxI4AgXIu96Jm1XzyQJdKptZNW4/LiFfXmuoCbTxsZCGramQj8y8Xa6Bc3jyZ5cWn6/hThP39rj2nZPCoVaZxq1fJKw92rWyqs26cMbLCvTBqtew/eNra5N2+4eoS0uaaEqa2LduOy0vjg58Lpp3ZQAABBBCQeA2U01t3zz33yOTJkx3v4tChQ+WZZ55xbKMydQI///yzNG7c2HFnXQq3f//+jm3BVH7++edy7bXXOnYtXbq0bNu2zbGNyvAJ6FKsjzzyiNxwww1SrVq1gAPrUrljx46VSZMmyZEjR7x9r7nmGvnqq68c9ytXrpxooGW4CoFy4ZJkHAQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgfQQIlHNxL1Esuzzbu4apdf/BMzLg5RWmukAbur+O418IlPPXCO97p0C5QgWyyqAnagU80Ky5W+XrH3e79rnvrkpSu0Y+U/vi3w7I5BkbTXVsIIAAAggELxDPgXK1atWSFSucfx748MMPpUOHDsFD0DNFgXfeeUfuuusux349evSQiRMnOrYFU6kBV926dXPsqvdZl9OlRE+gTJkycumll0rhwoW9/3Lnzi179uyRTZs2ef+tWbNGzp8/bzohzUinwXPWooF0efPmtVanaZtAuTTxsTMCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggkO4CBMq53ILs2TLJiIF1Ta2aeezRAUtNdYE2Xup3qeTKmWTqQqCciSOsG06Bcrrk66hBdSUpKdHxWFu2n5DhRpZAXbbVrWgmPM2I518++2qH/PebHf5VvEcAAQQQCEEgngPlkpKS5NixY5I1a1bbFS9fvlzq1jX//GDrREVIAro856BBgxz3SWug3FtvvSVdu3Z1HLtmzZqiS7NSYltAl9/t1KmT7SR/+uknadKkia0+LRUEyqVFj30RQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgfQXIFAuwD0Y+VxdyZY1k6nHv9/4Xf7cfMxU57RRtlROefJB+/JRBMo5aYWnbtvOkzJ0rP0P2rr0qi7Bai0XLoi89Moa2bbzhLUpebtU8RzydK/qydu+NyNf/102/pXyPPD15xUBBBBAwCwQz4FyeiUbN26U8uXLmy/K2Dp69KjkyWNedt3WiYqQBDp27CjTp0933CetS69+8cUX0rp1a8exdUnQ7du3O7ZRGRsCmTNn9macy5fPnPlXz+6NN96Q+++/P6wnSqBcWDkZDAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBCIugCBcgHIn+5VQ0oVNy+d+tPifTL9o80B9vq76Y6by8qVjQvb+l2sgXKZMydK/doFbB7hrNi89ZhoVjlraXFFUbmtbWlrtfy4cK/MmP2Xrd6/ot31peSaZsX8q+TEifPSd8jygFnoTDuwgQACCCBgE4jnQDkNztGMclmyZLFd19mzZx3rbR2pCFqgYcOG8ssvvzj212xinTt3dmwLplIzxlWvbg+I131z5sxp/D/fPZg+mPHpE1mBVq1ayZdfful4kO7du4tmDAxnIVAunJqMhQACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAghEX4BAuQDmLZsUlVtvNAdYnT59QZ4ygqR0GVa3okFhQ5+pI7p8q7VcrIFyVodobhcqkFUGPV5LRGe7X/ll2X6Z8sEmvxrz2zy5M8vAPjVtWQV/WXbA2G+juTNbCCCAAAIhCcRzoJwuybly5UrH6z1w4IAULFjQsS2UykaNGknfvn1T3GXXrl3Ss2fPFPvFcwcNTNy/f7/kzp3bdhkLFiyQpk2b2uqDrThy5IjjuKdPn5Zs2bIFO4xrv8GDB0uNGjVc230NEydOlLlz5/o2eQ1S4P3335cOHTrYemvAarFixUS/HsNZYilQTpeV7dOnT4qXt23bNunVq1eK/eiAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCFwMAgTKBbjLWbMkygt960iOHOaAtxVrDsmkd/+Uc+c9tr2TkhLkXx0rSq1q9iWgtDOBcjayqFTcd1clqV3Dck+M2zdh+h/y2+pDtnNITEyQHsZ9tO1j9Bw2bo1s3UGGGRsaFQgggEAIAvEcKBdoKdClS5dK/fr1Q5Bw7urmY+39559/SqVKlazVGW77448/FjWxFs3sV6ZMGTl48KC1KcVtXTpXl9B1KosXLxbNZJfW8t1330nz5s1THObhhx+WV155JcV+dPifQL169UTvU0KC5ZMQRpf//Oc/cuONN/6vc5jexVKgnAYIaqBgSmXt2rWuWRNT2pd2BBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQCCjCRAol8IdvfGaEnL91SVsvdasPyxvv79Jjp84l9yWM0eSdL29glSrkie5zvqGQDmrSHS2y5TMIX0fsi+tdt4Idpz52VaZ/8ve5KVU8+fLIh3alpHa1S2Bdcaprvr9sLw2ZUN0TpqjIIAAAhlYwC0Q7NChQ5I/f/6YvXJdbvXnn38WDdJxKkOGDJF+/fo5NYVU5+ZjHeRiCZS777775PXXX7devnf7hRdekP79+zu2BaocN26cPPTQQ45dxo4dK71793ZsC6WSQLlQtILvm5iYKF999ZVcddVVjjvdfffd8s477zi2paWSQLm06LEvAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIJD+AgTKpXAPcuVMksF9a0sWYzlVWzEyku3cc1J27z0lRQtnk+JFstuW97TuE0yg3BUNCsmNrUpadzVt582T2bR9/oJHjh37X9CeqfGfDQ3w8mVC0+wbr7xoznijAWPfLtht2rVooWwywFh+1L9MmGbPwlalQm7p3eMS/24y/NW1snnrcVNdem50vKWcNGlYyPEUDh85K8eOn5PTZ85L2VI5JVMme3YSXXb3pfFrvPfbcRAqEUAAAQSCFnALBAtnoFyhQoXk+PHjcvLkyaDPK6WOo0aNkkceecS12xVXXOENpHPtEGSDm49194slUK5s2bKyefNm6+V7t3X51HLlyoWUVU7nxl9//WVkDc7hOObtt98uH3zwgWNbKJUEyoWiFXzfkSNHymOPPea4gy63qvPh6NGjju1pqSRQLi167IsAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIpL8AgXJB3INGdQvK3beVTzEILoihglp6tcUVReQ2I6NZuIt/4NrFGCiXZAS/PXJfVSlfOmfotAGWaQ19MPZAAAEEEHALBAtnoNywYcOke/fu3iUtx48fL/v27UsT/C233CIzZ850XOpRB96/f78UKVLEyFB6IU3H0Z3dfKwDXyyBcnrdupzmDTfcYCXwbuuypbp8abDlpZdekieffNKxu87BUqVKeYMsHTuEUEmgnB2rePHikjNnTvnjjz/sjSnU6M+vTz31lLz44ouuPSO5jC2Bcq7sNCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACcSFAoFyQt6lZ4yJya9vSkilRyVIuy1cdlAOHzshVTYuaOgeTUY5AORNZWDc0Q+A9HQIvj2s9oGaSe/fjzbL4twPWJrYRQAABBFIp4BYIFu5Aub59+3rPULPKaYYwDXTTJRtPnz4d9Jlfeumlost7tmnTJuA+U6ZMkXvuuSdgn2Ab3Xys+19MgXKXXXaZLFq0yEqQvK33t1u3bgED3DJnziyaFfDBBx9M3s/6ZsSIEfLEE09Yq1O1TaCcnU2/Rt566y3v16EGOGoAZDDBpZpVcNKkSXL11VfbB/2nZvXq1VKnTh05f/68a5+0NBAolxY99kUAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEE0l+AQLkQ7kGp4jnk9pvLSIWyuVz3OnHivHz+3Q75+sfdcs2VxaTdDaVMfV96ZY1s2X7CVGfdqFc7v3S/s6K1Om3bRka0gSNWyr4DfwcGGAk5ZNizl4oGjvnK5BkbbcFguY32Ic/UMQUIjnxtrWzcYl5StXiRbNLvUb8lWo3jDRi+UvYfDD4QwXcekX7Va29+eVFvEGPB/FlcD6fL2a5ee1hm/Xer7N0fe9fheuI0IIAAAnEg4BYIFqlAOX8SXarz+++/Fw0y27hxo2zatCn5VfuVK1fO+698+fLSsmVLad++vWsWOd+4W7ZskQYNGsjevXt9VWl6dfOxDnoxBcrptf/3v/+V6667zsqQvL1q1SpvENyvv/5qWnJXA+Rq1aol48aNE10e162cOHFCqlWrJno/w1EIlLMraqDc5MmTkxv06++9996T9evXJ38t7tixw7ssbokSJUQDJO+44w5p3bq16H10Kx6Px9tn3rx5bl3SXE+gXJoJGQABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQSFcBAuVSwV+kUDapcUleKZAvi/GHczGyVnjknPFv+64TsmLNITl3zogSo8S8gN67SuVzS9mSOSV/3sxy4tR5yZ0rsxw7flYOHDwjK9cekqPHz8X8dXCCCCCAQDwKuAWCRSNQLtxep06dkiZNmsjSpUvDNrSbj/UAF1ugXNWqVWXhwoWSN29eK4VpWzOKrVmzxhsMqZnIatSoIVmyuAfH+3bu0aOHTJw40beZ5lcC5eyE1kA5e4/U1Tz22GPebIGp2zu4vQiUC86JXggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBALAtoVBf/MGAOMAeYA8wB5gBzIGpzwAgEMxJA2cvBgwfDdg7Dhg2zHyACNZ07dw7bOft+JuvYsWNQZ/rHH3+E/di+c4jV11atWnnOnj0blE8onWbMmBF2y19++SWoU3jooYfCfuxYvX9GoFxQJqF0ev7556PiZwTKOZ7W4cOHo3J8/3sarKMRMBr1c/M/T97zezZzgDnAHGAOMAeYA8wB5gBzgDnAHGAOMAeYA8wB5gBzgDnAHGAOMAeYAzE2B7ghMXZD+GMWgSrMAeYAc4A5kOHnQEYIlDOW6fREKsBp/PjxjgE51sopU6Zk+Lni9HNa9+7dwxosZywF6jGW9QyrZc6cOT1nzpyx3jLb9oULFzz16tUL67GdzGKlLtgALxuUQ4UGTD733HNRs4ulQLk333zTQcRepf1i5d5zHvzezRxgDjAHmAPMAeYAc4A5wBxgDjAHmAPMAeYAc4A5wBxgDjAHmAPMAeZADMwBbkIM3AT+gEVQDHOAOcAcYA5cVHMgGoFymult69at9siRMNT89NNPnksuuSRi90yzQKVUNAirQoUKETuHWP/5qH79+p6VK1emxBSw/fjx454nn3wyIoatW7cOeGxf40cffRSR48fq/bv++ut9l56m119//dVz6aWXRtUulgLlNJtkSuX06dOeMmXKRNUoVucd58Xv/MwB5gBzgDnAHGAOMAeYA8wB5gBzgDnAHGAOMAeYA8wB5gBzgDnAHGAO/DMHgOCLgTnAHGAOMAeYA8yB6M6BaATK+e5plSpVPA888IBn5syZnv3796cUW+LarkEn06dP9zRp0iSigSfFihVzPQf/hjfeeCOi5+Hzi+XXrFmzevr27etZtWqVP02K7zVAbty4cR61jtT1DR06NDJBYokAAEAASURBVMXzOH/+vKdmzZoRO4dIXVtaxs2RI4fnlltu8Wg2xFC/HjWD3FdffeXp1q2bJ1OmTFF3i5VAudKlS6c4t7SDzvG03Cv2je7/F/HGmznAHGAOMAeYA8wB5gBzgDnAHGAOMAeYA8wB5gBzgDnAHGAOMAeYA5GfAwn/IBsvFAQQQAABBBBAIDoCRqCcfPzxx7aDGYFDsmjRIlu9r8IIipL58+f7NkN+TUxMFCMLlVSvXl2KFi1q+7d9+3YxMsXJoUOH5ODBg7Jv3z5ZunSp95yWLFkixnKrIR8z1B3uuOMOee+99wLuZgTtSeXKlcXImBew38XUqPe0Xbt2UrFiRSlevLgYQXCi91O39T7qv99//12MQCtZsGCBqGEky8KFC6VRo0YBD6H3uWPHjgH7ZOTGpKQkufLKK+Wyyy6TkiVLJv8zAuIkS5Ys3nu0a9cu2blzp/z2228ye/ZsMYLrIkoyZMgQadGiheMxChQoIFWrVrW1HTlyRPLmzWurj1TF3XffLUagYcDhT5486Z37akdBAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBD4W4BAOWYCAggggAACCERdwC1QLqUTufnmm2XOnDkpdYvr9gkTJkiPHj0CXoORKUp69eoVsA+N6SeQJ08eOXDggBhZz1xPQoNCNbhv/fr1rn1oiL6AkXlS2rdvH9KBox0o9/bbb0uXLl0CnuPIkSPl8ccfD9iHRgQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEDgYhMgUO5iu+NcLwIIIIAAAjEgQKCc+03YsGGDVKpUybWDZrXTLGmaaYsSmwJt2rSRzz77LODJTZ48WYwlRAP2oTH6AvEQKPfXX39JmTJlXHGOHTsm5cuX92ZRdO1EAwIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCBwEQoQKHcR3nQuGQEEEEAAgfQWIFDO+Q6UKlUqxeVUhw8fLk8++aTzANTGhMCIESOkT58+rueiS4tWqVJFNm/e7NqHhvQRiPVAuQoVKsiff/4ZEEeXj+3Xr1/APjQigAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAhejAIFyF+Nd55oRQAABBBBIZ4GbbrpJZs+eHfJZZPSlVzt37ixTp051dTl69KhooMy+fftc+9CQ/gJLliyRevXquZ7Ia6+9Jj179nRtpyH9BN544w259957QzqBaC692r17d5k4caLr+R06dMibTU5fKQgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAmYBAuXMHmwhgAACCCCAQBQEEhISpG7dupIlS5aQjrZu3To5ePBgSPvEU+e33npLunbt6nrKgwcPlgEDBri205D+Avnz5/cGMiYmJjqezKlTp7xL627fvt2xncr0FciRI4fUrl07pJM4d+6cLF68OKR9Utt52rRp0qlTJ9fd+/fvLy+88IJrOw0IIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAwMUsQKDcxXz3uXYEEEAAAQQQiCmBTZs2Sbly5RzPSQMENZscmaIceWKmUrMefvLJJ67nM3r0aHn00Udd22lAIJCABliWKFHCsYtmmtTvEZp5koIAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIGAXIFDObkINAggggAACCCAQdYHy5cvLxo0bXY/br18/GTJkiGs7DbEhMGbMGOnVq5fjyRw/ftwbyLRnzx7HdioRCCRwySWXyO+//+7a5cknn5Thw4e7ttOAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCFzsAgTKXewzgOtHAAEEEEAAgZgQ6Natm0yaNMnxXPbu3esNsDp27JhjO5WxI7BixQqpVauW4wkNGzZMnn76acc2KhFISeD++++X1157zbHbrl27pGLFinLixAnH9khVJmbLLdnL1ZWshctJ5nzFJFPOfJKYJYdIYpLIhXNy4cwJOX/8kJw9tEtO790sJzcvkwunyHgXqfvBuAgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIBBYgEC5wD60IoAAAggggAACURF49dVX5YEHHnA81uOPPy4jR450bKMydgRy5Mghhw8flqQkI0jIUo4cOSKaNfDAgQOWFjYRCE5g4sSJ0r17d8fOmsVw3Lhxjm2RqEzKVVDy1G0jOSrUlwQNiguyeIzguRMbl8iRZf+Rc8f2B7kX3RBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACB8AgQKBceR0ZBAAEEEEAAAQTSJFCoUCFp2rSpXLhwQTweT/I/3f7uu+/k5MmTaRqfnaMj0KhRIylWrJj3/vnfS834tXTp0uicBEfJkAJFihSRyy+/PPl7g36f8M2xb775Rk6fPh2V685VvYXka/B/kpApc6qP5zl/Vg4t/kSOrfku1WOwIwIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCAQqgCBcqGK0R8BBBBAAAEEEEAAAQQQuNgEEhKlQNO7JGelRmG78uN/LJID86eJeC6EbUwGQgABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQTcBAiUc5OhHgEEEEAAAQQQQAABBBBAwCtQ4Mq7wxok52P1Bsv9ONW3ySsCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggEDEBAuUiRsvACCCAAAIIIIAAAggggED8C+hyq/kb3RaxCzm46EOWYY2YLgMjgAACCCCAAAIIIIAAAggggAACCCCAAAIIIICAT4BAOZ8ErwgggAACCCCAAAIIIIAAAiaBpFwFpdgt/SUhU2ZTfTg3POfPyq5Zg+Xcsf3hHJaxEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAGTAIFyJg42EEAAAQQQQAABBBBAAAEEfAKRWnLVN77vlSVYfRK8IoAAAggggAACCCCAAAIIIIAAAggggAACCCCAQKQECJSLlCzjIoAAAggggAACCCCAAAJxLJCYLbeUuP0FSUhMivhVeC6ckx3v95MLp45G/FgcAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBC5OAQLlLs77zlUjgAACCCCAAAIIIIAAAgEFcla+Qgo07RSwTzgbD8yfLsc3/BTOIRkLAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEgWIFAumYI3CCCAAAIIIIAAAggggAACPoH8V9wpuS5p6tuM+OuxdfPl4E/vRfw4HAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQuTgEC5S7O+85VI4AAAggggAACCCCAAAIBBQpf31uyFasSsE84G0/tWi97/zsmnEMyFgIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCQLECgXDIFbxBAAAEEEEAgWgIVK1aUtm3bhny4OXPmyMaNG0Pejx0QQACBYARuvPFGqVSpUjBdk/ucOXNGXn311eTtSLy56667pFChQiENvXv3bnnvvbRlZyt2S3/JnLeY63G7XlNLEhMSXdudGv7cdVC+W7nFqUnOHt4lu2YNdmwLtbJ8+fLSokUL227vv/++nDhxwlZPBQIIIIAAAggggAACCCCAAAIIIIAAAggggAACCGR8AQLlMv495goRQAABBBCIOYH/+7//k48//jjk87r55ptFg+UoCCCAQCQEZs6cKe3btw9p6CNHjkjevHlD2ifUzitWrJBatWqFtNvixYulYcOGIe1j7VzijhclU3b3a/tjwn2SJSmTdbeA258s3CC9Jnzl2Of8ycOyY8Yzjm2hVnbo0EE0KM5aBg4cKM8//7y1mm0EEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBC4CAQIlLsIbjKXiAACCCCAQKwJECgXa3eE80EAARUgUM48D0reNVISM2czV/pthTtQ7sLZk7J92uN+R0j920yZMsmaNWukShXz0rHHjx/31u3YsSP1g7MnAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIxKUAgXJxeds4aQQQQAABBOJbgEC5+L5/nD0CGVWAQDnznS119xhJyJRkrvTbCnegnOf8Odk2tbffEdL2tlOnTjJt2jTbIG+//bZ07drVVk8FAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIZGwBAuUy9v3l6hBAAAEEEIhJgdQGyt10003y6aefhv2akpKSpHTp0lKmTBm5cOGCHDhwwPtv3759cvbs2bAfjwERQCA2BQiUM9+XUveMk4SERHOl31bYA+U8F2Tb2w/7HSFtbxMTE2XVqlVSrVo100D6fb5BgwaybNkyUz0bCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggkLEFCJTL2PeXq0MAAQQQQCAmBQIFyo0cOdLxnHW5vKFDh8qpU6cc24OtLF++vNx2223ewAl9X65cOSlVqpToMn3WcubMGVm8eLHMnz9fPvvsM/nxxx+tXdiOA4EOHTpI8eLFXc906tSpcvDgQdd2GsIjoMGot9xyS3gGcxhlyZIl3q9Vh6agq+6++26pXbu2Y/+KFSuKfu+yliNHjkjevHmt1WHd7tOnj+scbtiwoTRr1sx2PP3epW1pKaW6viIJxn9u5elbLze+dzq3N6tRWqqWKmjb9ZOFG6TXhK9s9VrhMf7bNvkhx7bUVt5+++0yY8YM2+7fffedtGzZ0lZPBQIIIIAAAggggAACCCCAAAIIIIAAAggggAACCGRcAf2rhifjXh5XhgACCCCAAAKxKOAWKHfo0CHJnz9/2E85X7583uC4zp07S9OmTY0MSc6BHSkdWLMPaSDfu+++Kx4PP0Kl5BUL7Xq/f/jhh4D3vGrVqrJu3bpYON0MfQ5XX321zJs3L2LXOGLECHniiSciNr5mtJw9e7Zt/GgEytkO6lfx+OOPy/Dhw/1q/n4bjkC50l3H28YNtmJYlxbSsXl1W/dAgXLaeevkB237pKVCv9+vWLFCatasaRumXbt28sknn9jqqUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIGMKUCgXMa8r1wVAggggAACMS0QrUC55s2by4MPPiht27aVbNmyhc3kiy++kK5du8rOnTvDNiYDhV8gS5Yssnz5ctuyi9YjEShnFYnMNoFykXElUC5l1/bt24suq2stf/zxh1SvXp0ltq0wbCOAAAIIIIAAAggggAACCCCAAAIIIIAAAgggkEEFCJTLoDeWy0IAAQQQQCCWBaIVKPfpp5/KjTfeGBGKffv2yXXXXSe63CMlNgUGDBgggwYNSvHkCJRLkSgsHQiUCwujbRAC5WwktgrNKqdZIytXrmxr06WZP/zwQ1s9FQgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIJDxBAiUy3j3lCtCAAEEEEAg5gUyQqCcImuw3JVXXim///57zJtfbCeowW+aTS5r1qwpXjqBcikShaUDgXJhYbQNQqCcjcSxon///vL888/b2r755hvRuUlBAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBjC9AoFzGv8dcIQIIIIAAAjEnEEuBcufOnZPVq1fL9u3bvYFvefLkkUsuuUQqVqwounRnSmXTpk1So0YNOXnyZEpdaY+SgGaP+v77771BjMEckkC5YJTS3ifSgXIvvviiPPvss2k/UZcRbrrpJpk9e7at9ciRI5I3b15bfbQqCJQLTrpChQry559/OnbW7/nr1693bKMSAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEMo4AgXIZ515yJQgggAACCMSNQHoHyu3evVveffddmTdvnvz4449y9OhRm11SUpJcf/31MmbMGClfvryt3b9i4MCBjpmK/PvwPnoCPXr0kAkTJgR9QALlgqZKU8dAgXLdu3dP09gej0dmzpzp+LWcpoH9diZQzg8jiLfDurSQjs2r23p+snCD9Jrwla3eV7F18oO+t2F/nT9/vjRp0sQ27qhRo+Sxxx6z1VOBAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACGUuAQLmMdT+5GgQQQAABBOJCIL0C5XSJvddff10++eQTOXv2bFBW2bNnF12y7+mnn3btf+LECSldurQcOHDAtQ8N0REoXry4rFmzRvLlyxf0AQmUC5oqTR3dAuU0q2PmzJnTNHY0diZQLjTlWAyUu++++7z/D7BeiX7vLlmypJw6dcraxDYCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAghkIAEC5TLQzeRSEEAAAQQQiBeBaAfK/fzzz9K3b19v9rjUGk2bNk06derkunuXLl1k6tSpru00REfggw8+kNtuu810sD179siCBQukXbt2pnrfBoFyPonIvhIoFxlfll4N3jV//vyya9cux2W177nnHpkyZUrwg9ETAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEE4k6AQLm4u2WcMAIIIIAAAvEvEK1AOQ3MyZ07tzeDXFrVChYsKGvXrpXChQs7DjVr1ixp3769YxuV0RFo27atzJkzx3awrl27SrNmzURfnQqBck4q4a8jUC78pjoigXKhuer3aqeg2YULF8rll18e2mD0RgABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgbgSIFAurm4XJ4sAAggggEDGEIhWoFy4tfr06SMjRoxwHHbjxo1SsWJFxzYqIy+gAZGrV6/2LoHrfzTNJtikSROZNGkSgXL+MOnwnkC5yKATKBeaa+fOnR2zf3o8HilSpIjs27cvtAHpjQACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAnEjQKBc3NwqThQBBBBAAIGMIxCvgXJXXXWVfP3114434sSJE5IzZ07HNiojLzBmzBjp1auX6UAXLlyQBg0ayLJly+Stt94iUM6kE/0NAuUiY06gXGiulSpVkg0bNjjupFlBNeMcBQEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBDKmAIFyGfO+clUIIIAAAgjEtEC8BsoVK1ZMdu7c6WqbK1cuOX78uGs7DZERaNSokfz000+SmJhoOsBrr70mPXv29NYRKGeiSZcNAuUiw06gXOiumjVOl9O2lrFjx0rv3r2t1WwjgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBABhEgUC6D3EguAwEEEEAAgXgSiNdAOTU+ffq0ZMmSxZFbM8ppZjlK9ASSkpJkyZIlUrt2bdNBNRCmSpUqcvDgQW89gXImnnTZIFAuMuwEyoXuOnfuXLn++uttO/72229y6aWX2uqpQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgYwhQKBcxriPXAUCCCCAAAJxJRCvgXI5cuRwzRh34MABxwxFcXVj4vBkn3rqKRk6dKjtzHv06CETJ05MridQLpki3d4QKBcZegLlQncdMGCADBo0yLajx+Pxfh/3BdjaOlCBAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACcS1AoFxc3z5OHgEEEEAAgfgUiNdAuZo1a8rKlSsd0ZcvXy5169Z1bKMyMgKVKlWSFStWSPbs2U0H+OWXX6Rx48aiQS++QqCcTyL9XgmUi4w9gXKhu1577bXy+eefO+548803y5w5cxzbqEQAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIH4FiBQLr7vH2ePAAIIIIBAXArEa6Dcs88+Ky+88IKj+ZQpU+See+5xbKMyMgLz5s0TDb7yLxcuXPAGyf3666/+1UKgnIkjXTbcAuX0ZFq2bCmtW7eWOnXqSNGiRaVw4cKSkJAg+/fvF11Gd8+ePbJo0SL5+uuvZfXq1ely/jfddJPMnj3bduwjR45I3rx5bfXRqiBQLnTp/Pnze+eWzjFrGTlypKgpBQEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBDKeAIFyGe+eckUIIIAAAgjEvEC8BsppNjnNKudUbrjhBvnvf//r1ERdBAS6dOkib7/9tm3kN998U+69915bPYFyNpKoVwQKlAvlZLZv3y5jxoyRV1991XUp5FDGC7YvgXLBSv3db1iXFtKxeXXbTp8s3CC9Jnxlq/dVbJ38oO9tRF+3bdsmJUuWtB3jo48+kltvvdVWTwUCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgjEvwCBcvF/D7kCBBBAAAEE4k4gHgPl2rVrJ7NmzXK01oxXxYsXl3Pnzjm2UxleAc02tnbtWilYsKBp4AMHDkiVKlW8maJMDcYGgXJWkehvhytQznfme/fuFc3yqMGR0SgEyoWmHOuBcr/99pvUrl3bdlE//PCDNG/e3FZPBQIIIIAAAggggAACCCCAAAIIIIAAAggggAACCMS/AIFy8X8PuQIEEEAAAQTiTiDeAuWyZcvmDcwqV66co/WQIUOkX79+jm1Uhl/gnXfekbvuuss28AMPPCCvv/66rV4rCJRzZIlqZbgD5Xwnr4FyDz30kJw5c8ZXFZFXAuVCY431QLlvv/1WWrRoYbsoDcKtXt2eCc/WkQoEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBCIOwEC5eLulnHCCCCAAAIIxL9AvAXKPf/889K/f39H+B07dnizmB0/ftyxncrwClx77bXy+eef2wZdunSpNGzYUC5cuGBr0woC5RxZoloZqUA5vYhvvvlGrrvuOjl79mzErolAudBoYz1QbubMmdK+fXvbRWmGUM1aSUEAAQQQQAABBBBAAAEEEEAAAQQQQAABBDKigD4Xta7Ys23bNpk7d25YL7dy5cpSpkwZ25hff/21rY4KBKIpQKBcNLU5FgIIIIAAAgh4BeIpUK5ly5by1VdfSaZMmRzv3t133y2a4YwSeYEcOXLIqlWrpHz58qaDeTweueKKK2ThwoWmev8NAuX8NdLnfSQD5fSKXnnlFXn44YcjdnEEyoVGG+uBchMmTJAePXrYLkqDbbNkySLnz5+3tVGBAAIIIIAAAggggAACCCCAAAIIIIAAAgjEu8Dy5culTp06psvQv4O1bt3aVJfWjREjRkifPn1Mw+jfcxITE011bCAQbQEC5aItzvEQQAABBBBAQOIlUK5YsWKybNky0Venor84aIYz/cGeEnmBl19+WZ544gnbgSZPnizdunWz1ftXECjnr5E+762BcseOHZMPP/xQfvnlF9myZYv339atW+Xo0aPeT5lVqVJFLrnkEqlVq5Z07txZdAnklErHjh3lvffeS6lbqtoJlAuNLdYD5YYNGyZ9+/Z1vKiiRYvKnj17HNuoRAABBBBAAAEEEEAAAQQQQAABBBBAAAEEwi2gCQI++uij5KQN+pxcM7/t3r073IcSAuVCI/3111+9H6727dWvXz/59NNPfZu8xqEAgXJxeNM4ZQQQQAABBOJdIB4C5TSD3Lx586RFixaO3BrQU69ePdFl+iiRF6hbt67oLyPWzH6HDh3yLn27d+/egCdBoFxAnqg06teSLpH6/fffy9tvvy269GWwSxZXqFBBRo8eLW3btg14rps3b/bOh0gswUqgXEB6W2OsB8ppkJwGyzmV6tWry9q1a52aqEMAAQQQQAABBBBAAAEEEEAAgYtQQLMfbdq0KVVXfvr0aTlw4IAcPHjQ+2/Hjh3y888/y4IFC2TXrl2pGtN/p48//tj7nNq/btSoUd5naf51vEcgJQF9tpqQoOEj/yu6oo4+z6VEXuDLL7+UVq1aJR9I74d+kPzMmTPJdeF6Q6BcaJKTJk0yJWvQ/x9Uq1ZN9Ps7JT4FCJSLz/vGWSOAAAIIIBDXAvEQKPfqq6/KAw884Oisv5g0a9ZMFi1a5NhOZXgFNDhOrevXr28bWJfa1CU3UyoEyqUkFJ32woULS0pBjYHOpE2bNjJlyhQpWLCga7fu3buL3u9wFwLlQhON9UC5Z555RoYMGeJ4UfoAav369Y5tVCKAAAIIIIAAAggggAACCCCAwMUnoIFy58+fD/uFb9y4UTTQbezYsd7VFlJzAA26a9y4sWnX5557TgYNGmSqYwOBlAScVs657rrr5IsvvkhpV9rTKKArqkydOtU0yl133SXTp0831YVrg0C50CTLlCnjfV6cNWvW5B01q5zb8+XkTryJWQEC5WL21nBiCCCAAAIIZFyBWA+Ue/rpp+XFF190vQE9e/aU1157zbWdhvAKPProo/Lvf//bNuhvv/3mDZ4L5iEVgXI2vrituOOOOwIur6q/5GsGwnAXAuVCE431QLkRI0ZInz59HC+qQIEC3k94OzZSiQACCCCAAAIIIIAAAggggAACF51ApALlfJDnzp3zrr6gQRerVq3yVQf1SqBcUEx0CkKAQLkgkCLQpVChQvL777+bPhy+bNky798+nO5JOE6BQLnQFXXFm969eyfvqKvl6Aeut2/fnlzHm/gRIFAufu4VZ4oAAggggECGEYjlQDlNJa4Zq9yKBmy5BVe47UN96gXKlSvnfTiUM2dO2yBNmzb1LlFga3CoIFDOASWOqz755BO5+eabHa9AHx4UKVIk7MsiEyjnyO1aGeuBcm7fE/TBdJYsWSRSD6FcwWhAAAEEEEAAAQQQQAABBBBAAIGYFYh0oJzvwnUZP/3QcCgf0iZQzqfHa1oFnJ6HkVEuraop7z9t2jTp1KmTqWPr1q3lq6++MtWFc4NAudA19W8OmgXU/29V7777ru3ehT4ye6SHAIFy6aHOMRFAAAEEELjIBWI1UO7GG2/0prpPSkpyvEPvv/++3HnnnQRQOOpEpnLu3Lly/fXX2wZ/5513RIMagy1uQTG6f9WqVWXdunXBDkW/GBAoXry4rFmzRvLly+d4Nh06dJAPP/zQsS21lQTKhSYX64Fys2fPFr2n1rJr1y7R+UVBAAEEEEAAAQQQQAABBBBAAAEEfALRCpTzHU+fa91zzz1y4sQJX5XrK4FyrjQ0hChAoFyIYGHofu2118rnn39uGmnevHnSqlUrU124N6IVKKcfdr/qqqtMp68rBenfa+KxvPDCC/Lss8+aTj2UhA6mHdlIVwEC5dKVn4MjgAACCCBwcQrEYqCc/jD75ZdfSvbs2R1vyjfffCM33HCD6Kf6KNERcFti88iRI96U1hrQEmwhUC5YqfjpN2PGDLn99tsdT3jkyJHy+OOPO7altpJAudDkYj1Q7scffxT9vm8tK1eulNq1a1ur2UYAAQQQQAABBBBAAAEEEEAAgYtYwC1Qbu3ataIZtwKVHDlyeJdULFiwoJQpU0auueYab+BI7ty5A+0mH3zwgeuzL/8dFyxYIFdccYV/lQwcOFCef/55Ux0bCKQkcP78edG57l80kEv/bkKJjIA+i6xZs2by4Bqs2KBBA1m6dGlyXSTeRCtQLhLnnp5j5s2bVzZt2iT58+dPPo2FCxfK5ZdfnrzNm/gQIFAuPu4TZ4kAAggggECGEoi1QDkNivj+++9ds1MtWbJEWrZsKUePHs1Q9yGWLyZz5syybds27xKa1vPU5QdGjx5trQ64TaBcQJ64bBwwYIAMGjTI8dynTp0qXbp0cWxLbSWBcqHJxXqgnGYkrFatmu2iNCj66quvttVTgQACCCCAAAIIIIAAAggggAACF6+AW6DcihUrpE6dOiHD6Iomt912m7z44otSrlw51/379+8vmsEoUNFsTdYP/c2ZM8e7RGCg/WhDwCrQsWNH2/P4CRMmBJXZ0DoW2ykLtGjRQr799ltTx2hkk9MDEihnYg9p46WXXpInn3zStI8GK2t2T0r8CBAoFz/3ijNFAAEEEEAgwwjEUqBchQoVZP78+a5L7a1fv96bdWjv3r0Zxj8eLkQ/UamZ45zK+PHj5dy5c05NrnWaqrx69eqO7RpUdfDgQVubHmfDhg22eipiQ+DWW291XV5Vl+xt06ZNWE+UQLnQOGM9UG7fvn3eT3Nbr0qX2NZslhQEEEAAAQQQQAABBBBAAAEEEEDAJxDuQDnfuFmzZvVmfrMGXfjaNbtU8+bNRTPjUxBAIGMJfPTRR3LLLbeYLkqXXJ4yZYqpLhIbBMqlXlUDk3X5WP8SbAZQ/314n74CBMqlrz9HRwABBBBA4KIUiJVAuRIlSniD5MqXL+94H7Zv3y5NmjSRv/76y7GdysgJBAqUi9xRzSPrMghff/21uZKtmBHQlPSamt6pRCLdOYFyTtLudbEcKFe6dGnZsmWL48kPHjxYNFthakpSvqJy/ugB8Zw/m5rd2QcBBBBAAAEEEEAAAQQQQAABBGJUIFKBcr7L7du3rwwbNsy3aXqNVoYp00HZQACBiAro80ldwjNTpkzJxzl58qQULVo01Ssb6d+7KlWqJPqaL18+0eQPW7dulXXr1snhw4eTj6NvMmqgnAax6Soiev07duwQXVXkzz//NF27b0P7XXLJJV5z7bNo0aKg7VetWiU1atTwDeVN7KBJOdSbEh8CBMrFx33iLBFAAAEEEMhQArEQKFegQAH54YcfTD/M+iNrtqFmzZrJ2rVr/at5HyUBAuWiBB3Hh9FfRPUXUqeyePFiadiwoVNTqusIlAuNLpYD5XRpE/2Un1PR7JP6ADo1pfC1D0u2ElXl/Mkjcmr7Gjmzb4uc2fuXnNlvBFsbnwCnIIAAAggggAACCCCAAAIIIIBAfApEOlBOVV599VV54IEHHIEuu+wy+fXXXx3bqEQAgfgT0GWXn376adOJz5gxQ+68805TXUobmTNnlvvuu086deokjRs3dux++vRpmTVrlowbNy55edBwBcpVqVJFevXq5Xhct0rNlPnwww+7NQesVzP9fuhf9Hvnxo0bZfr06dKoUSP/Ju/7zz//3Pu9dfPmzd7t+vXry9ixY0WXS/UvFy5cEF1O+957703x+62eh95D//Lyyy+LBj1T4kOAQLn4uE+cJQIIIIAAAhlKIL0D5XLlyuXNFGb9gdqHfPToUbnqqqtEg20o6SNAoFz6uMfTUdu1a+f9Bd/pnCPxSVsC5Zyk3etiOVBuxIgR0qdPH9vJnz171vtpwxMnTtjagqko0Wm4ZMqSw7HruWMH/hc8ZwTOnd2/zbEflQgggAACCCCAAAIIIIAAAgggEHsC0QiUy5s3r/dD28WLF7cBBFrWL1u2bKasVLrzmTNnRJ9zOJUsWbKIBtf4igaHaCYra9FgkhYtWkjJkiW9/3SZ2J07d8q2bdu8H0CfP3++8bnAwB8M1NVaNBilTJky3jHOnTvnXb1FV3DRbP86xoEDB6yHTtV2QkKCXH755dK0aVNvRi119J2zZnnSf99++633/EM9QM6cOU27nDp1Ss6fP2+qy5Mnj+jzw4oVK0qpUqWkSJEiXh+91iVLlnj/1vD777+Leqel6HVpMFC5cuW8/zQzmZbdu3fLrl27vP90pRxdKUXPM5RivU7d1+lagxlTM6Xp/de/wWiWtGLFinmfu2lmMz1X/acu33//vTcbVzBj+vfRr8ns2bP7V3nnsdW3cOHC0rZtW6+V3pdChQp5751mENO//+g5/PHHHynOZdOB0rih81K/jvRc/MuNN94o//nPf/yrAr5v0KCBvPXWW1KrVq2A/XyNajNo0CDRFTWWLVsmderU8TV5X7/66itp3bq1qS6ljZYtW8o333yTUjdTu37f0PuXmjJ79mzv15n/vv369ZOuXbt6v/b86/3f69e/fm8oW7as11j//uRWDh48KHpd1uVV/fvrOJoRUL/v+Irup1+Px48f91XxGuMC+n8w/mHAHGAOMAeYA8wB5kDU5oARKGf8LGwvxg+SET8H45cQj/FLov3g/9QYARIeI5NcxM+Dn78C//xp/KLieo+i1XD11VczD2L4+6LxqS3XqTB58uSw3zvjQZfj8YyHO2E/VijfHx5//HHH8zI+ZZzm8yrddbwntf+mf7fa8bw+/nl9wDFDufbU9v3xxx8dz+2nn35KtVlittyeUnePDnhtVstitwz05Gt8mydHpUaerEUqeBISk1J9/NRasF/g/xfhgw9zgDnAHGAOMAeYA8wB5gBzgDnAHGAO6BwwgjocnyUYgRRh/V3+/vvvdzyOEUzmMQIyHI/1888/2/YZOHCgY1+9FiMwydTfCF7zGEs1evvrMYysdp7Vq52f6/jvaASJeLp37247jlo99NBDHmOlFv/uju+ND6x7jCVnPUYwk22cYL/28ufP7zE+FOkxlll0PIZ/pV6rkVnLY3xIPujjXX/99f5DeN+PGjUqef/KlSt7pk6d6tG/K6RU9u/f77n99tuT9w32GrVf8+bNPUbApMcIgEzpMN52nTNG1ixPzZo1gz6e08DXXntt0PvreZYvX97z5ptveoxlP52Gs9XpeU6ZMsVjLIEZ0nF69OhhG8v4YGzyGPXq1fN89NFHHiNo1NbPWmEEUXmuueaa5H1DuS+p6WtkjbOegmfPnj2epKTgnw3qHDaCGG3jBFPx2muveYyMcrauX375ZcgGRkCZbZyUKoyAvZCP43M2AuVSGt61/bPPPvMYWeVc2/0b9H4Ywa8Bz9PpGbPOS9+58hrbPz+QUc6YoRQEEEAAAQQQiK5AemWUM37RkJkzZ8rNN9/seMH6KTs9t7lz5zq2R6tSPxEWTIpm/YRYz549o3VaUT1OemeU009RGr9Mi37KL7VFP5mly4OmVCZOnJjucy6lc4zFduMBlHTu3Nnx1IYMGSL6SbJwFjLKhaYZqxnl9P8DR44csX3iVK/OeDBrW/IgtKsWScyeR7KXrC6ZC5WRLIXKev/5f7IwpfFO79rw95Kt+4wlW42lW88d2288YkzbJ41TOibtCCCAAAIIIIAAAggggAACCCAQWECzH1kziOkeukyfNStT4JECtxYoUMCbtU2zvllL7dq1ZeXKldZq71KK1iUXn3vuOW/mKFtno8IIgpPq1aubmvQZpmZDevvtt0POKGUE3XiXXtRscZp97p133vFmYzIdIIUNze6vWb9CzUylS07qc8CCBQumcAR78xtvvOE9b82+F6jo80d9Dulf9BrvvvtuueWWW8T4wK5oNrlQio5nBBOKrmyTUtFMg0aAjzcbVkp93donTJjgXZ7Trd1XbwQK+d4mv1533XXyxRdfJG+7vcmRI4f0799fHn30UW8mP7d+bvX6txldGtQI8pRjx465dUuud1r6cujQofLMM894r3XMmDEhnYde++jRo73PBnWp0kiWSZMmSbdu3UyH0K89zYoWTNFsj999953oyknhLPGaUS6cBv5j6fcX/dpxKzrX//3vf5uaNeOd/o2REvsCBMrF/j3iDBFAAAEEEMhwAukRKKcPM/QX0E6dOjl6atrpO+64Qz788EPH9mhWuvlYz0HTg1eqVMlanWG29cFQuIr+kt2xY0fH4TT9/7p160xt+oAkmF/ITTtZNvSXVeOTfpZa++bDDz8sr7zyir2BGlcBnRsaxKip852K8ak8mTFjhlNTqusIlAuNLlYD5TQAVpdUcCrGJ4Tl888/d2pKU12mnAUkW8lqkqXw34FzWQqUCno8jxEkd3rneiNoTgPn/g6eO3/8YND70xEBBBBAAAEEEEAAAQQQQAABBNIuEK1AOT3TOXPmeIPGrGetgVXjx4+3VoclUE6XGdQlHI1sYLbxg6kwstCJBojoUpZGdrpgdrH10aU4L730Uu/SobZGS4XeD32eqsdNS1m4cKHocpdGpjfXYdwC5X744QcxMqe57pdSgy59qQFPTsFpvn01AM/I8uVdatVXl9pXXxBZoP2dziWYQDl9VqtLhloDNgMdy61Nn9vpMzojI51bF2+9W6CcBrlpoGhqi16HzolIFl3qVZfo9S+h/I3AyGQWMHBSn5svWLBAdJlbXZ5Vl7/VJZpTKqkJlNN7Pm3aNNehdXlZDfb0LzrP9Gs4NcVp6VXfOPq9c9GiRd6lnrt06RLwmvVrXpe01mWK9W84rVq18g2T/KoJNdq0aZO8bX2jS7nqvfAv+gFt/XpwCqz278f72BAg/Z/xGX3jVvAPA+YAc4A5wBxgDkRpDhiBYMbPwvYSyaVXNZ20W9FUz8andWLm/rv5WM/f+IUqZs451n+WMh70WPmSt0NN6x7stRqBcsnHCPRGlyIIdkz6/f0ze6D7qUso6JIL4bZi6dXQlmGN1aVXe/Xq5fjlqPNGl3wO97xxGy8pTxFPrmotPAWuvNtTrF1/Y9nWMUEv26p9C13zgCdP3Rs82UrV8Oiyr27HoZ7f85kDzAHmAHOAOcAcYA4wB5gDzAHmAHOAOZD2OWAEdTg+Twj30qt6r4wgH8djGRmnHH//D3XpVadlVXX5y7QUXVJULdJavv76a8drtM5hI6NbWg+VvL8eM1OmTK7HNQLlkvv63hhBbkEtterr7/Z66623uh43a9asnp9++slt11TV63Mxq6X/ttOgKS29qsvmOs0pp7GCrVu/fr2nWLFiAc/VCJSzDWcESXmMACVbfagVRvBXwGP7m4X6vlSpUo6nY3yYP6hjXnnllY77a6Uud1y2bFnbOEaGSo+RYc91P19DapZeTen6dVlka4nE0qt6ff7nYgS4efR5r1Mxgtk8ujSvr78uOW2sRGXraqzolNzH19f/1cjo5zjfLr/88oD7+Y/B+7T//zm1hmSUM+QoCCCAAAIIIBBdAbeMaYcOHRIjwCXsJ/PSSy/Jk08+6Tpu7969ZezYsa7t0W5w87GeR0bPKGe93rRs6yci3VKXV61a1ZZRLi3H8u1LRjmfRHhfr7rqKjEeYLkOqp/iatasmWt7ahvIKBeaXKxmlNNPU2oWSWvRTHL6adX0LFmLVTaWav1nydaCZSRT7oKSkBDcpysvnD0tp3asTc46d9bIPnfhzMn0vByOjQACCCCAAAIIIIAAAggggECGEYhmRrl//etfjpnK3LIbGYFytkxeoS69ar1Rf/31l3fJwf/+97/ezF6aJU6XftWsdsEuNXv8+HFv1relS5d6V4bQTF/VqlXzZkd77LHHxGl5WT2P4sWLB8wqd8MNN3izl1nPWbeNwBZ59913RbO1GYF7kjlzZqlbt643q5ZmhsuZM6fTbjJy5Eh5/PHHHducMso5ddRsepqRS5fHXbVqleiKJcYHtKVDhw6uq9zo0r2aRc+I0LENqUu7TpkyxVavFTq+rqYxb948b0YsXbbUCCyTWrVqyT333CP6/NQI/rHte/LkSa+vZhpzKk7nkVJGuUAZvvQY33//vfffr7/+KppJrUKFCtKwYUMxAr7k6quvdjoNb51m0tNjO52TdnDKKOc0mO/4el902WH9Wtb70r17d9G55FQi+ZxQVzyyZmDT1Y6MD/CKLkGcUtHvA07PMPX6rrnmGtmzZ4/rEMOGDZO+ffu6tqcmo5zrYP80GIFy0qdPH1M3vafhzCin81qXX9ZX/+L29xk9pyeeeMK/q9SsWdNxaWv9utJsl25Fs/fpfPIvunzw888/71/F+xgVIKIxStlzjPuPNQbMAeYAc4A5wBww5oBbxrRIZJR79tlnjZ+73Yu2x9r/o918rFdBRrngf7YKlIGMjHLBO6bma0U/QbV161bP4MGDHT/RFsqY+mm+zZs3W78UTNvGErsR+Zomo1z8Z5QzHsSZ5or/ht7fUOZiVPomJHqylqjqyV37Wk/Bq3p4ind4Ieisc6W7jveU6DTcU7BFV0/umld7shat5ElIyhx718jPhdwT5gBzgDnAHGAOMAeYA8wB5gBzgDnAHIiDORDNjHJGcJD/I4vk95o5zul5RDgyyiUfxHhjLD3pmnXfCDTzzJo1y7+74/vPPvvMU6ZMGcfz1WvQ54WarcmpBMpgplnWNmzY4LSbNyOUESzjekx9LuS2AohmIatevbrjvk4Z5fxPwAhS8+jfGHSOON0frTOWdfQcO3bMf7fk99rmtJ9munMq48aN8xgBgI77+MYxPkTsMYLhnHb33Hfffa77Ou0Q6H4YQZ1Ou3jrjEBJT7du3VyPped61113eY4ePeo6hrEcqev+Thnl/AfS4we6Vj3+nXfe6Zp1zAjqdD22zzk1rxMmTPA/Te97zcgXzFhGUKVtX63QuWUscRrUGNOnT3ccQyvjNaOc8cF5x2s3Emg4Xmvbtm1t/TWrnBFoZ+vvn3nO6R4Zgbm2fYxlmW3jO+1LXWT/LhWEb7qfABPF+EYcxI2iD07MAeYAc4A5kGHmgFsgWLgD5fQXqUBl6NChMWnq5mO9FgLlgv8ZikC54K3C/XOppo33FX3opA/bNCgp0JIG1nPQX/QnTZrk0bTsgcry5cs9+kutdf9wbBMoF/+BcgMGDHCcPlu2bAlpPoZjPqV2jIRMSZ5spWt6l10tdE1PT4k7h4UWPHfHUO9yr7mqNfdkKVzOY3y8OCJfL6m9PvZLv+/V2GPPHGAOMAeYA8wB5gBzgDnAHGAOMAfc50A0A+WMzEaOzy/WrVvn+Dt8OAPlRo8eHTDgS+dIgQIFAi47qs/+gplL999/v+N1GpmeXPd/5plnHPcxssG57uN/LvrcUK/RqXzwwQeOYwQKlNMlHBs1auS4n/9x9f0jjzzidFiPWzCYPq+yFg0uDBSQ53/Myy67zLq7d3vq1Kmu5+u0g1ugnAbrbdu2zWkXz5o1a1wDD/3PUd9XqVLFY2TWcxzHyI7m0eBI6z66HShQTs/LWEXGcT/rWE5Lg+rJtGvXLqj9reOltK3LylqLLiWc0n7a7hSUpWMZmQeD2l/H8H9Wbz2PeA2Umzx5suP1a6CkU3GbG7p0rbUYWQcdx/bdLyNbnnUXj5FN0qPLsvr68Or+/9b0tGHpVUOfggACCCCAAALRFTACweTjjz+2HTScS6/mzZtXjMA7xxTjvgNrOmrjp1jfZppf+/fvL3PmzEnzOG4+1oFZetUq4r7N0qvuNpFu0WUudblLazEeWHiXB9i0aZP4/9u5c6focg7GQxLvP10aV5caSGlZZk1R37p164DLslrPIZRtI1BOdCkBazEeiIl+v0mvostCDB8+3HZ4Xe5BlzFISzGykqV691hcetV4ECWVK1e2XZMRQCdGxkNbfbxUJCRllWwlqxrLtpZLXro1MUv2oE//3LEDcmr7GmPZ1i1yZv9fcnb/tqD3pSMCCCCAAAIIIIAAAggggAACF4OAEZwkxgdAbZeqS2cGuxSpbWeXinz58nmfa1ub9+7dK0WKFLFWS7iWXl2yZIn3WVIwz8vdnrUama3EyMwmxuoStvO0VhiBJKLPAfXVv+hyo7p8qLWULl3au4Rrjhw5TE3ffvutd6lRU2WADV3yVa9Vl1r0L3rdlSpVko0bN/pXS6ClV41McvLiiy+a+rtt6BzSZ6BGpj1Tl5dfftlxOUxdTjUpKcnUV5fCdVsu1NTxnw1d7rRBgwampkWLFtmW6vV1cLr3bkuv3nHHHfLee+/5dk1+1We0upys/u0l2KJLV+pystbr1f27du0qb7/9tm2oQEuvdunSRYyAQNs+ThU6n/bt2yfZs5ufpfXq1UuM7H1Ou6S6TueeLkFsLU8++aTj811rP/17kC5day0tW7YUI1uitdp12wgIE33mbi3xuvSq29dQ+/btZebMmdbLFCPY1/F7rP4NQ/+W4V/0e5HbEsjaT5c5NrI/+u/ifW8Eqop+/VFiW4BoRiN61rhF/MOAOcAcYA4wB5gDUZoDbhnTwplRTj/ZFu1i/AIWljnk5mO9HjLKBf/zGxnlgrcK98/FgT6lZp3TadkO9pOjqb0+MsrFd0Y5/XSvU9HlMYoXLx6W792pnVuR2C8xaw5PjgoNPPka3uIpcsOjnlJ3jwop81yxWwZ68jXu4MlRqZEnc77inoTEpAxnFAl3xky//9dgjz1zgDnAHGAOMAeYA8wB5gBzgDkQyTkQzYxyeh0nTpywPcY4cOCA4+/m4coo16JFC8fxnVzdssHpUodO/d3qjKAt23UawVeOYzhl/lKnihUrOvZ3O6bW169f33HJTc36Zt3PLaOcEQzoMYKrbP2t+/tvGx+yt12vZgnz7+N7r9nUrGX//v2eYsWKOfb37ef/+sADD3iWLVtm+hfoHlmPp9tuGeXmzZvn1N3z5ptvBn1+/ueqS8o6lfnz5zuO55ZRbunSpSGvOKL7WIsRfOV4XP9zDvV9yZIlrYfxbhvBWCkeS78HaaYyazECO0O+Xs3a6FTiNaOczgWne3HNNdfYLlNXvFFLp/5z58619U9p+V7NTudU2rRp43gMp+NSl24/v6TbgZkcUQpG4IuLOc4cYA4wB5gDsTYH3ALBCJT7e6527NjR6WdrWx2BcsF/badHoNwvv/xiu2dOFQ899FCG/rk4GoFyr776asQNCZSL70A5t4dtxqcKIz53YuX/wYnZ83gD3zQArsiNj3tK3fNK0MFzpbq+4g2408A7DcBLyl3YWLbV+aFSrFwv5xH8/yOxwoo5wBxgDjAHmAPMAeYAc4A5wBxgDgSeA9EOlDNWXrE9Rty8ebPjM4xwBMppoFAoc+Dmm2+2nZ9WGFnGQhrn008/tY3jFii3fPlyW19j1ZqQjud/jRqAZS0a/OXfR9+7Bco99thjtr7Wfa3bGnxlLd9//73jOD/99JO1q3d7w4YNrsFr1uOFuu10QKdAOV121SmY8+jRoyEF8vmfX8GCBT369yFr0eAwp4BEt0C5Dh06OHr6H8v63mlJ0+nTp4c8jnVc67aRac96ed5t/XuZta9121iBxXHfGTNmpLivdSy35/XxGihnZP9zNGjSpInN7Pjx44591eijjz6y9Xcb22eqgatOxciE6Hoc3768Bv7/bhR80v0EmCQEzDEHmAPMAeYAc+AimwMEygX++Wv8+PFOP1vb6oyUz3ztBPm1E+1AuZw5czp+wst6E41U9J569epl6Pvo9ou31SI120aqes+DDz4YFT8C5eI3UC5btmweY3kSxymmnyyMwi/dMXuMTDkLeHJWaeLJf8WdnqI3Px104JyxLK+nVJexnsLXPuzJW/9mT/aydTyZcuaP2eu8mO8x1x74Zy588GEOMAeYA8wB5gBzgDnAHGAOMAeCmQPRDJRLSEjwaNYja/ntt98cf+8OR6DcG2+84Ti2m03Tpk2tp+fdrlWrVkjj6PNta3EKlCtcuLBHn6NaS1pWmBg6dKh1OI8Gelmv2S1QrlWrVra+1n2t2wMGDLAdc+HChY7jjBkzxtbXv0Kz0915552e/PnD9zzGf3zfe6dAObeVGyZOnOh4LVYHt+1XXnnFd1jTa/PmzW3jugXKVatWzdbX7Xi++gkTJpiOpxuR+HCtzhmnEkw2x8svv9xpV4/OE991BPuqWRidSrwGyvXs2dPRwGmeahCym9P7779vY0npe4w+d3Yqffv2dT2O2/Gpj+7PIwn/gBsvFAQQQAABBBBAIDoCRqCcGJ/2sh3M+CFVjF/sbPWpqTCWXhUjFXlqdk31PkaKbDF+uU/1/r4d16xZI8YvdL5Nx1djuUAx0jqLkVrbsZ1Ks4ARKCfGp3jMlf9sqeO6desc21Jb2bp1a/niiy9S3H3WrFnSvn37FPvFc4fGjRuL8cAu7Jfw66+/ipGNT4zMfWEf22lAI1BOZs+ebWs6cuSI5M2b11YfrQrjl3UZPny47XCLFy+Whg0b2upDqTCCoULpbuo7rEsL6di8uqlONz5ZuEF6TfjKVu+r2Dr5Qd/bsL327t1bRo8ebRvPWN5DjIdMYjzMsLVdzBVJeYpItpLVJEuhssa/MmJkj5OETElBkXjOn5NTO9bJmf2b5cy+LXJm719y4dTRoPalEwIIIIAAAggggAACCCCAAAKxKmAEyokRvGY7vRUrVkidOnVs9WmpyJUrlxgBW7YhfvzxR2nWrJmtXp+76fM3//Lcc8/JoEGD/KuS369evVqqVzc/s9HnSyNHjkzuk9Ib44OxsmDBAlu3PHnyOJ67reNXjXjAAABAAElEQVQ/FZMnTxZ9pu5fjEA5MVZc8a/yXreRec1UpxtG1jXvP1tDEBX6/LZ27dq2nsYHoMXIlpZcbwTKydSpU5O3fW/Klft/9u4EPq6q7v/4b/Y0e5ukSUrbhNKFVTZZZBMEfWR/2B4XEEVBUERAVhVEWRT4syrihoI8ZRFkUUHEBRHBRwRFpCzdm65Z2zRLk1nv/5yZJJ07cye5kyaTOzOfyyuvmXvm3Dvnvs9NaZPv/E6ztLS0DO/aelRBubR50T+fSp0/fTJVqUpU9TjR98NoWyQSERW2E31/6HtBf3V2do52SMbXrH5G9tGPfjTt59znnnuuqCVW086jlvQUtURuWrvdBvWBaFFhubTuqqqXqNUiTO0qKCff/va3TW36e7S0tFRUFTpT+1g7Kign5513nqmbqi4mp59+uqltR3fOPPNMWbx4cdpp9t13X1EVE9PakxtUtUbR3xup29e//vU0h9Q+qfv6Hu/r60ttlj/84Q+ivy8mctP3w2WXXWY6pb7P9J+p49n0z+f1z+mTN7U8qug5TN3UEsuif0aevOnfGdbW1iY3jTxXlQVFhU9H9vUTfZ/dfPPNprbUncHBQQkEAqbmO++8U1TVSVMbO84TIM1osxKJmjqsMOAe4B7gHuAe4B6YgHsgFxXl9Cc5VIDF6sMck9am/tG4w/dHplLNqYPO9lN+xf73GKtPCGpTXbpdfyJxon0yvV/yPOpPhu65554T/t4TfS0Tcb6mpiZD/bDBeOyxxwz1j9Fkhqyeq4Co8bvf/c740Ic+lHM3KsrlZ0U5vTTDpk2bLO8zXe1wIu7vYjhHoH6+UbHn0UbNkecYjad9Uy3b+j3b1ed2OusOo+ZD5xkV7/svIzBrV8PtL8V9Av4uVQz3HdfIz6C4B7gHuAe4B7gHuAe4B7gHuAe4B5xyD+Syolym5Rn1MqVWHioYlfZzj+uuu86yrz5eBeXS+l966aUZ+1u9Z6YVJFSoK6vzqKBc2lisKsqpDxqn9ZusBhWAM12DVUW5gYEBQ98TVjajtWVTUU6f59RTT7VcjnSsa1cBTkMFlAwVejL070lGG1Pya1bntaood/XVV1t1NU488UTb75X8vsPPM1VcU8HPtPNaVZRbuXJlWr/hc4/2mKuKcpdccomlW+o9ZzVWXaHMavvc5z43rmvW1RNTt3ytKKeCm5YGKoCYeonxVUesfHWbCjGm9df3eqb+w+2tra1px+lzDb/OozP/LuMamhj1wIYAAggggAACCORGIFNFOf2JH/0Jqkyb+seAvPzyy5leLoj2TJ8MSr44tdykLFiwQNatW5fczHMHCehP8anS3qOOyOrTkaMeUCAv6k+Lvf/975cDDzww/slI/enI+vr6+HNdCdLj8Yj+ZOtOO+0kmzdvjleGXLZsmehPjepPiVp92m2iaG666SY58sgjLU+nx6arD6Zuuagopz+1qkrip751fF87qSBi2mu5qCj3iytPFr/Xk/beuqFpZqXUVpamvZbrinLqB71yxx13pI1DhTblYx/7WFo7DTYFXG4JNC6MV5wbqTxXNsPmwSLR0DYJbnhXVZ1rSVSe61wjRiRs+3g6IoAAAggggAACCCCAAAIIIJBLAf3zrFxVlFPBF1FLWKZd3r333iu64lbqNhEV5XTlI10Bye6WqaJcRUVFVj+7s6oo9+ijj6ZVddIVo374wx/aHd4O9dM/001ewcKqolx7e3v855nZvlE2FeWGzz1nzhx54IEHRH1weLgpq8eOjo74z8a+//3vj1ntT6V90s5tVVHu1ltvFV09LnXb0ZVb9M8416xZk3raeDU5XVUuebOqKPevf/1LdBWxbLdcVZS74YYb5JprrkkbXk1NTfzn4GkvJDXoio9WFcouuOACUUUVknrae6qdU3+mnK8V5XQ1QKs/M3WlPn1PJG+62qIqnJDcNPJcV/vTVf+SNzsV5fRqRQsXLkw+bFKq85negJ0JESDNqBKiSpIvDLgHuAe4B7gHuAdydA9kqiiX9rGLlAZd0anQ/59t9emlFAbju9/9bsE75PM8qyUGDFXyPnXaTPv6dfWPJ+YxR3/m2L2ffvnLX5rmyc7O1q1bJ30e9adAs93U0rQ7PC619OqoVcOC4dHvc6sxP/V/y0Y9p925stNPV5Oz+kSfKodv2Pmkpp33oM/2f0er5VmNktl7GJX7HmfUHvMFY9Ynbh51rlPvr1kf/44x4/CzjfLdjjT8dc2GuLL/ZDbzsX0+sMCCe4B7gHuAe4B7gHuAe4B7gHuAe4B7YOLugVxWlPvBD35g9SMV45xzzrH8WU8xVJTTFe9ytZ1wwgkmZ6uKcm1tbaY+dr/Xsq0oN3xel8tlXHzxxYaumDbeTa/yoT6kP+q4rc5tVVFOV3iz2tSyp6Oef/h6Mj2qD1BbndZQgcq081pVlPvnP/+Z1i/TeyW3W/1ORv+cOLnPRDy3mn99wXZWvFHLzFraqGWKxzVOFaBMOx8V5cZXUW7JkiVpli+99NK45mUi7jPOYfv//bY7MpkO+0UeNzn3LvcA9wD3APdAvt4DBOUy37vLly9P+0t1ckN/f7+hl2fN17kvhnEff/zxyVNm+fxnP/sZc+jAv1sTlDP/2ZQaZErdd3pQTn3K0vL77+abb+b7L0fffy5fwJjWtLdRtf9JRt1/XWTMOvP/ZRWeazzjBmP6oZ80yhYeavhq5jBvOZq3Yvh/Nddo/vMeDzy4B7gHuAe4B7gHuAe4B7gHuAdGvwdyFZTT77NhwwbLn2e8733vs/x3cTEE5fQSpLnajj32WJOzE4Jyyd+fu+22m3H55ZcbL774ohEOh7Ni0f31z66Tz5f83OpkVkE5VVHOqusOfzB87ty5luf93ve+lzbmfAzKqepvltenVg9Ku77kedHPMy29mu2yyfpcXq/XiMViaWMhKDe+oFxLS0ua5VNPPTXmnKbOMfuj/394Enxy/obcFPxwm3uAe4B7gHuAe6DI7wGCctZ//5o9e3baX6hTG/Q/QifhL4SccwK/J2+77bbUaTPth0IhqllNoPdEfj8QlDP/2ZQajEvdd3JQTn+CVX+6N3XTbbrq40TeN5zLfN+M5eEOlBqlO+9vVB9wqjHz2EuM2WffmVV4ruHU64zqg//HKJt/sOGrbjRcbi/z6dA/U8e6F3g9u+8dvPDiHuAe4B7gHuAe4B7gHuAe4B6YunsgV0G5Y445JvVHGfH9bdu2xcMtVvdAMQTldDjMalNLLho6QDhRX2rp0LSfMTgtKJd8D5SXlxv6ntEV3tSymUZvb68Vk6lN30v77bdf2nXq81ptVkG5q6++2qqrceKJJ1qeN3nMoz3/8Ic/bHlefX2px+VjUC5T4POAAw5Iu77U6/3CF75gaXPLLbeMeWzquWbNmmV5LoJy4wvK6RVnUjddpTDVnf2p+394BnvHDYibhh9ycw9wD3APcA9wDxT4PaCXUB3PVuhLr1r9ozvZqaenx6itreX7w+HfH7rE+2jbvffeyxw6dA5/9KMfjTZ1lq/lYunVP//5z5bvPVrjRCy9Ovuce0YNMLVu6RttCJavjbb0qn6/DP9ozbpdLyVhtZ1//vlZn2uixsR5Mv/swT2t0iidf5AKwJ1hzDzhcmP2Z7436r2XHNrU983M4y41qg88zSid937DW1nHsq0O/TOW74HM3wPYYMM9wD3APcA9wD3APcA9wD3APeDseyBXQblnn33W6scZxnPPPZfx5xnFEJTz+XyW1dP+53/+J6PLRH1PWf3MPtdLr9q9Fu2kfyb26quvWt5Hw4233367pdvw68mPVkE5HVC02i677DLL89od/4UXXmh1WuOiiy5KO28+BuUOPfRQy+vTAcGxjM444wzLYx944IExj009tw5KWm0E5bIPyunlgq2q8910001Zz0vqPLE/uX8vcA0Bqwc2BBBAAAEEEEAgNwIul0v23Xdf8fv9Wb3h0qVLZcuWLVkdk0+d1XKccs4552Qc8g033CDf+MY3Mr7OC1MvMH36dOns7BT1wzPLwQwODsr8+fNFLaFg+TqNUyugqpCJ+gRqVoOIRCLy+uuvZ3VMtp1nzJghCxcuzOqwvr4+WbJkSVbHpHZWYSVxuazvZd23vrpMdqopTz1s1P3NvYOypn2rZR/DiMn6By6yfC3bRv09qD6Nqcav/8m7fVMBQolGo9sbeOZYAW95jQRm7Sr+2rnqq0n8NXNsj9WIRSXYulxCnWvVV0v8K9pfuH9/sA1DRwQQQAABBBBAAAEEEEAAgXEJ6J8zWP084T//+Y/svffe4zpn6kFHHXWUvPDCC6nN8f3TTjtNnnzyScvXVFBODj74YNNrqgKXfOtb3zK1De+8/fbbsvvuuw/vxh+/8pWvyJ133mlqG23nkEMOkVdeeSWtS0VFheifSdnd7r//fvnMZz5j6v7oo4/KJz7xCVOb3nn33XdFVXwztavlKEWtwGJqm+gdFZSTBx980HTa9vZ2qa+vN7XZ2dE/20+dFxVqS5s/O+caq88HPvABufvuu+M/H0vtm+k9VXgqtat89KMfleeff97UftBBB8nf//53U5ve+elPfyrnnntuWrvdBrXEqnzpS19K637kkUfKX/7yF1O7CsrJt7/9bVPbv/71L9l///1NbXZ2VPUvUeE/U9cnnnhCTj/9dFPbju6oJVZl2bJlaadRgU95/PHH09qTGxYtWiTvvfdeclP8+csvvyyHH354WvtoDSpMKY888khaF1WVUD7ykY+kte9Ig1p9R1SA0nQKfZ9l+v2JqaPFzq9+9StRBTVMr+i5u++++0xtekf/DlLfE8mb/t1NXV1dctPI88WLF8uZZ545sq+f6Pvs5ptvNrUl7+jfB23evDm5Kf78kksuiX//pb1Ag2ME9G8N0v/Ec8zwGAgCCCCAAAIIIFA8AqtXr5bm5mbLC9YBwXnz5kl3d7fl6zQ6Q+Dkk0+Wp59+OuNg7rrrLrn00kszvs4LCDhJYPbZd4vL483ZkIxoRNY/eHHO3o83yj8Bb1W9lCSF57wVdbbvUSMalsGNyyTUlQjO6RBdbKAn/xAYMQIIIIAAAggggAACCCCAQM4FJjsot/POO8tLL70ks2fPTru21tZWmTNnjugPa1ptxRKU00HBU045xUSgg4VHH320qc3uzhFHHJH2ofWWlhbRIcPkbSqCcjoQqJbaTB6GrFy5Ur773e+a2sbaqampiX/AN/V3DuFwWKqrq0Utw2o6hd2gnKpcF/89hf7QcfKmln4VHQZTFfeSm20912NdsWJFfFzJB+ixVlVVycDAQHJzPMCUb0E5fR1Wv9/JFPRKvmD9Z5D2TTXXc6b//ND3rt3tmWeekeOPPz6tO0G57INy2n7VqlVpljpw9/DDD6e10+AcAYJyzpkLRoIAAggggAACRSyQ6S/UwyTXXHONqHLNw7s8OlRAf0rvy1/+suXo+vv742FH/YlDNgTyQWCns24Xt68kZ0ONhQdkw+LLc/Z+vFFhCATq5yeqztXpqnNzxVNRO2olxOSrjoWDMrjpPQl1tEh4qPpcLGT+IXFyf54jgAACCCCAAAIIIIAAAggUp8BkBuV0CE6H5FLDTMPSN954o1x77bXDu2mPxRKUu/766y0d1NKgopaMTHMZq+HXv/61nHjiiaZuVtW5piIo953vfEeuvvpq09h0pT696oQOjmWz3XPPPaKWNE07RFcoS61uZjcop0+mQ1XHHHNM2nl1Za/U6mxpnSwaMlWTs5oTfXg+VpTT49YrzgQCAf10ZNOVBvVqQmNt+s8Jq+pxdo/X5581a5asXbtW1JKhaW9HUC77oNyBBx4oukJj6qYr82lPNucKEJRz7twwMgQQQAABBBAoIoHPfvaz8dLkVpfc0dERD1hlU7be6jy0Tb6AXm5hr732snwjXaJb/wOeDYF8EZj18W+LZ1pVzoYbHdgqGx/9Ws7ejzcqUAG1XHCgcWEiPFejwnN1KjxXNl1c6j87W1QF5YIb3h1asjWxdKsRCdk5lD4IIIAAAggggAACCCCAAAIFKjBZQbmFCxfKs88+K/Pnz7eU0xW29tlnH9EfwM20FUtQTldZ08uvpm5vvPFGfLlNq5BXat/hfR0W0hW4vF7zSgoXXHCB/OhHPxruFn+ciqCcDprp5UBTN6slSFP7pO4/9NBD8slPfjK1WfSSkanVzawMrZZe1SfTy4X+4he/SDtvLBaT/fbbT95888201zI16Ll966230uZD9//0pz+dtvStbs/XoJxeClQvCZq8PfXUU3LqqacmN1k+14FHHXxM3XRFM/1niNX8pfa1chvuQ1Au+6Dc5z73OctlX3UAev369cO0PDpQgKCcAyeFISGAAAIIIIBA8Qnce++9aeXUhxUuv/xyuf3224d3eXSogC57vnXrVst/0Pf09MRLoG/evNmho2dYCKQLNJx6rfiqGtJfmKSW8NZWaX1y7E9PTtLbc9oCFtBLCAcaF6nwnArOxb9UeG5ape0r1iHOwQ2q8pyuOqeXblUV6MSI2T6ejggggAACCCCAAAIIIIAAAvktMNFBuaamJtFVoM4++2zLnyVqrWg0Gq8epYNwo23FEpTTBn/84x8tl1r93//9X/niF78odj5orn+GqwNBhxxyiIk1FApJY2OjpP78diqCcnpsr7zyiml8euf111+XD3/4w2kBt7SOQw16iVS9dK+uRJe8aaeKiorkpvhzq6BVpqCcDhnqgJYOBKVuS5cujQfp9IfKx9p23313eeyxx2SPPfZI66qXcJ07d67ouUndrAJfOoS2//77p3Ydc1+HElOr4D3xxBNy+umnj3lsth1uueUWufLKK02H6dBmpoqSyR1ra2tl06ZNln9m6POmViFMPlY//8AHPiDPPfdcfCnb1Nf0PkG57INy3//+9+N/9iR76mWSM4Wfk/vxfGoFCMpNrT/vjgACCCCAAAIIxAX0P3IOO+ww0Z+40v8gHf7S+y+++KIMDAwglQcCBx10kDQ0NMTnL3ku9Q8k9D/U2RDIJ4G6Yy+WkoaFORvy4Kal0vG77+bs/Xij4hZw+QJSMmvXkeCcTwXoPP5S2yiRvi4Z3DgUnutUS7d2rbN9LB0RQAABBBBAAAEEEEAAAQTyS2BHgnJ6mcXKykrR1eP0z3/1l16Wz+/3j4pw3XXXiV5udKytmIJyuqLaCy+8IC5XetV4vYzoxz72Mfn3v/+dkUyHrnQoSi/XmrrppVhPPvnk1GaZiqCcvj4dQrMKT+mfMevqYzpcNdqmq+bpANqhhx6a1k1XMTzhhBPS2rMJyumDddDz5z//edp5dINeYlQXANBBokybDqfdddddosOLVtv5559vWVlP983XoJwOOlotFax/P9TV1WXFYGp78MEH4/ekqXFoR1teeumlVi/JBz/4QXnmmWekvLzc8nXdSFAu+6Dc3//+d9G/E0refvazn4muNMfmbAGCcs6eH0aHAAIIIIAAAggggAACCEyJwPRDPiHliw7L2Xv3LX1ZtvztkZy9H2+EQKqAO1CqwnO7jYTndPU5l3f0X1wknyO8tU2CSeG5SG+HGNFIcheeI4AAAggggAACCCCAAAII5KFApqDcZFyKDitdccUVtlcYKaagnPbWYaCLL77Ykj4YDMpLL70kejlW/fXOO+9ISUlJPHCmK6OdddZZoquspW7btm2TI444Qv75z3+mvjQlQTk9iEsuuUTuvPPOtPHoBn2P6MDgI488Inp5Xl1lTFdd08up6gptOox14oknplWSGz7ZcccdF68sNrw//JhtUE4fp8N4Z5xxxvAp0h5fe+21+JzoRz3WefPmyQEHHBCvlnjwwQen9R9u+M1vfiMnnXTS8G7aY74G5fT9uGXLlvh9mXxROjyrg2pjbbrqoa7YZ1URUB+riy7oIJ6uSKhXv9HWOpyrl8qdNm3aqKfPJii3aNEi0ffRWJueQx1wTd2+8pWvpDal7euKgg8//LCp/Ve/+lXafaEDl/fdd5+pn97RS9ymFi/o7OyUurq6tL66YfHi7IJyHo9Hent701x1gFRXuWRztgBBOWfPD6NDAAEEEEAAAQQQQAABBKZEoGzBITLjsDNz9t6bX35I+pf/LWfvxxshYEfArZZojYfn6uaOLN3qcrntHCqqPqyE2lYmlmxVVedC6ivSqz4dzLKttvzohAACCCCAAAIIIIAAAgg4RSBXQTldhUuHLB5//HHbl15sQTkd9tEhsdFCVrbxVEe9Ksgpp5wiuqKc1TYVFeWGx6ErU51zzjnDuxPyqMN3mUJK4wnK6XDen/70p3goaUIGqE6yZMkSOeqoo0SHmjJt+RqU09ejA2nHHHOM6dL0sql6+VQ725e+9CX53ve+Z6drVn2yCcp98pOflIceeiir82fb2WopXScF5fbcc09566230i5LV65ct46VJ9JgHNZAUM5hE8JwEEAAAQQQQAABBBBAAAEnCLhLKmTWx24Ul9s76cMxYhHZ+ItrJDbYO+nvxRsgsKMC3vIaCcSXbR0Kz9XMsX1KIxaVYOtyCXWtlVCHDs+tlWj/ZtvH0xEBBBBAAAEEEEAAAQQQQCD3ApMdlItEIvElLG+44YYxl9RMvfpiC8rp6y8rK5Mnn3wyvoRtqke2+zp0NNryoFMZlPN6vfEA37HHHpvtZVn2v/XWW+Wqq66yfE03jicop4/TSws//fTT8XCb3t+RTVdC09XwdNW10bZ8DspdeeWVaaG4J554Qk4//fTRLtn02m233SaXXXaZqc3ujl6eWFe223XXXU2HEJTLrqLcpz/9aXnggQdMhqtXr45XTTQ1suNIAYJyjpwWBoUAAggggAACCCCAAAIITL3AjMPPlrL5B036QPpXvCqb//rgpL8Pb4DAZAl4q+qlpHGR+OuaxFejvirrxOWxFzI1omEZ3LQsXnFOV53T4bnYQM9kDZXzIoAAAggggAACCCCAAAIIZCkwWUG5jRs3ym9/+1vRAably5dnOapE92IMyukr9/v9ogNHuhKXDs5lu7333nty3XXXxZcOHe3YqQzK6XHpa7v33nvl4x//ePyaRxtrptd0EPOmm26Sb37zm5m6xNvHG5TTB+v50JXqvva1r2VcFnS0N9fL3+rvA11VTVdWHGvL56CcVSWyvr4+qa+vF+1gZ3O5XPE51d8DeglQu5uu0jZcQXHvvfc2HUZQLrug3C9/+Us57bTTTIY/+clP5POf/7ypjR1nChCUc+a8MCoEEEAAAQQQQAABBBBAYMoFdOWshlOvVYEf36SNRYeEWp+8QSJ9aklKNgQKRUD9wDIwc97Icq3+2rniqagVu8u2xsJBFZ57T8I6ONehqs91tUgsaO+HpYVCyHUggAACCCCAAAIIIIAAAk4R0KGUtrY2qaury3pIOvTT1dUV/9JLSba3t8vf/va3+HKV77zzTtbnSz3gqaeekv/+7/82NX/hC1+QH/7wh6a24Z23335bdt999+Hd+KMOOOklOe1uhxxyiOjKX8lbd3e3zJw5U8LhcHLzqM/vv/9++cxnPmPq8+ijj8onPvEJU9toO7NmzZIrrrgibtDc3Dxa1/hrS5cuFV2575FHHokvuzrWARMZlDv//PPT5kUv+XryySePNYx4iEoff8EFF0hjY+OY/XWHf/zjH7J48WLRph0dHWMes2nTJmloaDD123fffUVXILO76bCXvp/0Pblw4cIxD9MVuHQ1ujvuuEPWr18/Zv/hDhMZlNP3wzXXXDN86vijDideeOGFpraJ3PnLX/4iRxxxhOmUejlTfV9msx100EFy9913i34cbdNBvPvuuy8eLA0Gg/L888+nVWTU94q+3+1sRx99tPzxj3+003XcfZ577jk57rjjTMf/+Mc/lvPOO8/UdtJJJ8lvfvMbU5vemTNnjqxZs0Z00Hl403/+6aCi1Xb77benLUv82c9+VvSfU6lbVVVV/P8JgUDA9NKBBx4or732mqmNHWcKEJRz5rwwKgQQQAABBBBAAAEEEEDAEQLlux8p0w86Y9LGsuXVx6XvnRcn7fycGAHHCKgfzAUaFqrwnFqyVVWdi4fnyqeLS/1nZ4sG+yW48b2hynMqPKdCdEYkZOdQ+iCAAAIIIIAAAggggAACCCBQ8AJ77bWX7LfffvHAng7t6SVBdcBx5cqVI1+tra2WS4zmC47P54sH0GbPni36S4eBdDhNh5900E1fn37UFQp1CG0qt0WLFskBBxwQH58eow4X9fT0xOdEB0Z1dbMlS5ZM5RCn7L11kFCHXJO3Z599Vk444YTkJtvPFyxYIKeeeqroRx0era6ujocj165dGw86PvbYY9Lb22v7fHQcXeBzn/tcPHiY3OvVV1+Vgw8+OLmJ5w4WICjn4MlhaAgggAACCCCAAAIIIICAEwQmawlWllx1wuwyhqkU0MuzBvSSrbWJ4Jx+9EyrtD2k6MBWGdygw3OJqnM6PKc+Em/7eDoigAACCCCAAAIIIIAAAggggAACCORWQFc502HGefPmjbyxXiJXh9zsVP8bOYgnUyLwwgsvyFFHHWV677POOkseeughUxs7zhUgKOfcuWFkCCCAAAIIIIAAAggggIAzBFxumXHYWVI2f/Qy/tkMNh6Se3mxiEGoJxs3+ha+gMsXkJJZu6qqc6ryXF2T+HR4zl9q+8L1MsaDG5cOVZ5rkXDXOtvH0hEBBBBAAAEEEEAAAQQQQAABBBBAYPIFLr74YrnrrrtMb3TRRRfJPffcY2pjx1kCupJjS0uLaUlXXbly7ty5Egqx8oOzZivzaAjKZbbhFQQQQAABBBBAAAEEEEAAgSQBvQxr9fv/W1weX1Jrdk+NaFi6X3+a5VazY6N3kQu4A2UqPKcrzzUnlm5V4TmX129bJby1LbFsa5eqPNfRIpHeDjGiEdvH0xEBBBBAAAEEEEAAAQQQQAABBBBAYOIEKioqZP369fElgofPyvKdwxLOfbziiivk1ltvNQ3w+uuvl+uuu87Uxo6zBQjKOXt+GB0CCCCAAAIIIIAAAggg4CgBb3mNVO57vJTO219cbq/tsRmxiGxb9U/peeNZ0RWv2BBAYMcE3GqJ1pJZuyWCc6rynK5A53J7bJ3UEENCbSsTS7aq5Vr10q2R3k4qPNrSoxMCCCCAAAIIIIAAAggggAACCCCw4wK33XabXHbZZaYTHX744fLyyy+b2thxhkAgEJClS5dKU1PTyIDC4bA0NzfLxo0bR9p44nwBgnLOnyNGiAACCCCAAAIIIIAAAgg4TsBdUiHTmveVQF2z+KobxFNWLW69PKQOz6lQXCy0TaL93RLubpVgxxoZWPOGxAZ7HXcdDAiBQhLQQdZAvPKcCs7VzhVvVb36lgzYukQjFpVg24qhJVsTleei/ZttHUsnBBBAAAEEEEAAAQQQQAABBBBAAIHsBGbMmCHLly8X/Ti8vfLKK3LYYYcN7/LoIIFLLrlE7rzzTtOIdHW5q666ytTGjvMFCMo5f44YIQIIIIAAAggggAACCCCAAAIIIDAuAb9aplV/+ermqqpzKthaWaeWT7ZXDVIvlTy4adlQ5bk18cfYQM+4xsFBCCCAAAIIIIAAAggggAACCCCAAAJmgQsvvFDuueceU+OJJ54ozzzzjKmNnakVqKyslJUrV0ptbe3IQPT+XnvtJQMDAyNtPMkPAYJy+TFPjBIBBBBAAAEEEEAAAQQQQAABBBDYcQGXSwIz5yWWbK1tjj96VCU6u8u2xsKDEoyH5xLBuZBaujUW3Lbj4+IMCCCAAAIIIIAAAggggAACCCCAQJEJeDweeeONN+KBq+FLf+utt2SfffaRWCw23MTjFAvceOON8vWvf900imOOOUb+9Kc/mdrYyQ8BgnL5MU+MEgEEEEAAAQQQQAABBBBAAAEEEJgcAbdbAvULxF+nqs/VqMpzqgKdp3y6uNR/drZosF+CG98bqjzXEn80IkE7h9IHAQQQQAABBBBAAAEEEEAAAQQQKGqBo446Sl544QWTwac+9SlZvHixqY2dqRFobGyUFStWSGlp6cgAHnjgATnnnHNG9nmSXwIE5fJrvhgtAggggAACCCCAAAIIIIAAAgggMOkCLo9PAo0LhyrP6eVb54pnWpXt940ObJXBjUsl1KGCc11rVXhujaiPQts+no4IIIAAAggggAACCCCAAAIIIIBAsQg8/vjjcvrpp49c7urVq2XXXXeVUCg00saTqRH4wQ9+IBdccMHIm7e3t8tuu+0mmzdvHmnjSX4JEJTLr/litAgggAACCCCAAAIIIIAAAggggMCUCLh8ASlpXBSvOOdTwTl/XbN4/Ns/TTvWoCJ9XYnwnA7OdayRcNe6sQ7hdQQQQAABBBBAAAEEEEAAAQQQQKDgBZqamuSZZ54RvRSr3vr6+uSkk06S1tbWgr92p1/g888/L3PmzBkZ5nXXXSc62MiWvwIE5fJ37hg5AggggAACCCCAAAIIIIAAAgggMKUC7kCZlMxKhOf0kq06QOf2BmyPKby1TYKbVOW5zsSSrZGedjGiEdvH0xEBBBBAAAEEEEAAAQQQQAABBBBAAAEEELArQFDOrhT9EEAAAQQQQAABBBBAAAEEEEAAAQTGFHBPq1ThuV3jlef0kq06QOdyJz4RPdbBhmFIqH3VSHBOB+givZ0iBsu2jmXH6wgggAACCCCAAAIIIIAAAggggAACCCAwugBBudF9eBUBBBBAAAEEEEAAAQQQQAABBBBAYAcFvOU1EohXnksE57xV9bYrzxmxqATbVqjwnFqyVQXnwupRL+PKhgACCCCAAAIIIIAAAggggAACCCCAAAIIZCNAUC4bLfoigAACCCCAAAIIIIAAAggggAACCEyIQGKp1ibx16nwXI1atrVyprg8XlvnNqJhGWxdJqGORHhOB+hiAz22jqUTAggggAACCCCAAAIIIIAAAggggAACCBSnAEG54px3rhoBBBBAAAEEEEAAAQQQQAABBBBwloDLJYGZ89RSrYngnL+uSTyqEp3dZVtj4UEJblLhORWaS3ytlViw31nXyGgQQAABBBBAAAEEEEAAAQQQQAABBBBAYMoECMpNGT1vjAACCCCAAAIIIIAAAggggAACCCAwqoDbLYH6BYnwXK2qPqdCdJ7yGeJS/9nZoiooF9y0VFWeU+G5LlV9Tj0akaCdQ+mDAAIIIIAAAggggAACCCCAAAIIIIAAAgUmQFCuwCaUy0EAAQQQQAABBBBAAAEEEEAAAQQKWcDl8UmgcaE5PDetyvYlRwe2yuBGFZ6LV55bK2H1aMSito+nIwIIIIAAAggggAACCCCAAAIIIIAAAgjkpwBBufycN0aNAAIIIIAAAggggAACCCCAAAIIIDAk4PKVSIkKz/mGqs751aMnUGbbJ9LXlQjP6apzKjgX6W6j8pxtPToigAACCCCAAAIIIIAAAggggAACCCCQHwIE5fJjnhglAggggAACCCCAAAIIIIAAAggggEAWAm4VlCtpXCT+uiYVoJurKtA1idsbsH2GRMU5tWRr51B4rqddjGjE9vF0RAABBBBAAAEEEEAAAQQQQAABBBBAAAFnCRCUc9Z8MBoEEEAAAQQQQAABBBBAAAEEEEAAgUkScJdWJcJzSZXnXG6PrXczDENC7askpKvOdegAnao819spat1WW8fTCQEEEEAAAQQQQAABBBBAAAEEEEAAAQSmVoCg3NT68+4IIIAAAggggAACCCCAAAIIIIAAAlMo4C2vkUC88pyqOlfTJN6qenH77FWeM2JRCbatiFedC6vgnK4+p5dxZUMAAQQQQAABBBBAAAEEEEAAAQQQQAAB5wkQlHPenDAiBBBAAAEEEEAAAQQQQAABBBBAAIEpFPDFK841qeVaE0u2+ipnisvjtTUiIxqWwdZliSVbdeU5VYEutm2rrWPphAACCCCAAAIIIIAAAggggAACCCCAAAKTJ0BQbvJsOTMCCCCAAAIIIIAAAggggAACCCCAQCEIuFwSmDkvEZxTVed0kM5bUSN2l22NhQcluEmH5xJV5/RjLNhfCDJcAwIIIIAAAggggAACCCCAAAIIIIAAAnkjQFAub6aKgSKAAAIIIIAAAggggAACCCCAAAIIOEbA7ZZA/XwVntOV5xLV5zzlM8Sl/rOzRQf7JKgrzw1VndPhOSMctHMofRBAAAEEEEAAAQQQQAABBBBAAAEEEEBgHAIE5caBxiEIIIAAAggggAACCCCAAAIIIIAAAgikCrg8Pgk0LhR/jVqytW4oPDetKrVbxv3owFYZ3Lg0Xnku3Lk2/mjEohn78wICCCCAAAIIIIAAAggggAACCCCAAAII2BcgKGffip4IIIAAAggggAACCCCAAAIIIIAAAghkJeDylSTCc0NV53T1OU+gzPY5In1difBcVyI4F+luEyNC5TnbgHREAAEEEEAAAQQQQAABBBBAAAEEEEBgSICgHLcCAggggAACCCCAAAIIIIAAAggggAACORRwq6BcSeMi8dUOVZ6raRK3L2B7BHqZ1tBQxTn9PNLTLkY0Yvt4OiKAAAIIIIAAAggggAACCCCAAAIIIFCMAgTlinHWuWYEEEAAAQQQQAABBBBAAAEEEEAAAUcJuEurVHhOLdta26y+VIBOVZ5zuT22xmgYhoQ6VseXao2H6DpUeK63U8SI2TqeTggggAACCCCAAAIIIIAAAggggAACCBSDAEG5YphlrhEBBBBAAAEEEEAAAQQQQAABBBBAIO8EvOU1atnWRSPBOW9Vve3Kc0YsKsG2FfHKc+GhCnSR/s0qPGfknQMDRgABBBBAAAEEEEAAAQQQQAABBBBAYCIECMpNhCLnQAABBBBAAAEEEEAAAQQQQAABBBBAIAcCPlVpbrjinH70VdaLy+O19c5GNCSDrTo8t33p1ti2rbaOpRMCCCCAAAIIIIAAAggggAACCCCAAAL5LkBQLt9nkPEjgAACCCCAAAIIIIAAAggggAACCBSvgMslgbqdxV/XJL4avWRrs3gramwv2xoLD0pw07J45blEgK5FYsH+4vXkyhFAAAEEEEAAAQQQQAABBBBAAAEEClaAoFzBTi0XhgACCCCAAAIIIIAAAggggAACCCBQlAJutwTq54tfB+fqmuMV6DzlM8Sl/rOzRQf7JNi6XEIdayTUtTZegc4IB+0cSh8EEEAAAQQQQAABBBBAAAEEEEAAAQQcK0BQzrFTw8AQQAABBBBAAAEEEEAAAQQQQAABBBCYGAGXxyeBhgUqNDe0dKuqQOeZVmX75NGBrTK4camEdXCuI7F0qxGL2D6ejggggAACCCCAAAIIIIAAAggggAACCEy1AEG5qZ4B3h8BBBBAAAEEEEAAAQQQQAABBBBAAIEpEHD5SiTQuDBecS4eoFMV6Dwl5bZHEunrksH4sq2J4Fyku1WMCJXnbAPSEQEEEEAAAQQQQAABBBBAAAEEEEAgpwIE5XLKzZshgAACCCCAAAIIIIAAAggggAACCCDgXAF3oExKVHjON1x5rrZZ3L6A7QGHOhNLtSYe10ikp12MKJXnbAPSEQEEEEAAAQQQQAABBBBAAAEEEEBg0gQIyk0aLSdGAAEEEEAAAQQQQAABBBBAAAEEEEAg/wXcpVXx8Fxi2dbE0q0ut9fWhRmGoZZqXS3DwTn9GOnpEDFito6nEwIIIIAAAggggAACCCCAAAIIIIAAAhMlQFBuoiQ5DwIIIIAAAggggAACCCCAAAIIIIAAAkUi4C2vGVq2NRGc81Y12K48Z8SiEmxfqQJ0LRIeqkAX6d+swnNGkehxmQgggAACCCCAAAIIIIAAAggggAACUyFAUG4q1HlPBBBAAAEEEEAAAQQQQAABBBBAAAEECkzAVztX4lXnatRjXZP4KuvF5bFZeS4aksHWFUOV5xIBuui27gIT4nIQQAABBBBAAAEEEEAAAQQQQAABBKZSgKDcVOrz3ggggAACCCCAAAIIIIAAAggggAACCBSqgMslgbqdZSRAV9sk3opacbk9tq44Fh6QoA7Pqcpzoa616nGNxIL9to6lEwIIIIAAAggggAACCEy9gEv9m6BpdqnMqPZLVaVffD639PSEZMvWsKzbuE22DUSmfpCMAIEiESid5pWa6X71FZCaGeqxOiAxVdm9vTMo7V2D0t4RVN+bIVXsnWrvRXJLFO1lEpQr2qnnwhFAAAEEEEAAAQQQQAABBBBAAAEEEMitgA7J+et3Ef9Q1Tldgc5TPkNc6j87W3SwT4XnlqvKcyo8F/9aK0Z40M6h9EEAAQQQQAABBBBAAIEcCZSVeuWYwxvkoP1qVEDOZ/mukYghb73bLX/6a6usXscHYiyRHNY4XQUeP3xEg+yxsCptZL98dl18PtNeoGFKBcrLvLL/+2bEvxebZpeNOZZQOCYv/V+7/O7Pm2RgMDpmfzogkI8CBOXycdYYMwIIIIAAAggggAACCCCAAAIIIIAAAgUi4PL6JFC/QC3bOrR0q3r0lFbbvrrowFYJblymqs7p8JyuPNciRozKFLYB6YgAAggggAACCCCAwAQK7NJcLp/92DypVqEqO5sRE/ntCxvluRc2UcnKDtgU9KmvK5GPfLBBDti3Rjxu6w85PfDoanntza4pGB1vaSXQPKdMPnpUo+y+qCrjnFkdN9zW1x+RZ/+4UV7+R4fEYlSYG3bhsTAECMoVxjxyFQgggAACCCCAAAIIIIAAAggggAACCBSMgMtXIoEGFZ6ra0oE6GpU5bmSctvXF+nrksFNKjyng3Oq8lxka6uqPBe0fTwdEUAAAQQQQAABBBBAIHuBBTtXyEXnLhxXMOflVzvkkadbsn9Tjpg0gTmzSuNhq733mC5qFd1RN4Jyo/Lk/MWT/2u2fOTIhh1+3zff3iI/Xrxyh8/DCRBwkgBBOSfNBmNBAAEEEEAAAQQQQAABBBBAAAEEEEAAAUsBd6BMShoXik8t1zpcfc6tAnV2t3hoTled6xgKz/W0iRGl8pxdP/ohgAACCCCAAAIIIDCaQHWVX7560e6il3q02gaDUfF6XOL1uq1ejrc98lRLvIJVxg68kBMBHXj8ryMbZbeFlbbfj6CcbaqcdJyooJwe7OO/WScv/q0tJ+PmTRDIhQBBuVwo8x4IIIAAAggggAACCCCAAAIIIIAAAgggMOEC7tIqKWlYmKg8V6OWblUV6Fxu61/Mpb65YRgqNLd6pOpcvPJcb4eodWVSu7KPAAIIIIAAAggggAACYwice+Yusu+e09N6rd2wTZ787TpZvqo3HpTbpblCDt6/Rg5Uy3imbuFwTK695S3p7Q+nvsR+jgSOPrxeTj1uTtbvRlAua7JJPcAqKLd5S0iWLO2Wja0DsrFtQDapx3DUkPraEpm/c7kcd/QsKStN//d0VPW57Qfviv5eZkOgEAQIyhXCLHINCCCAAAIIIIAAAggggAACCCCAAAIIIBAX8FbUJpZtrVXBOVV9zlvVIG5fwJaOEYtKsH2lhPWSrR2q+lzXWtHLuIoK1bEhgAACCCCAAAIIIICAtUDjzBK55pI9RVKW51zV0if33L9MgsH0D6OcdVqzfOD9tWkn/ONLrfLUc+vT2mnIjcAJH54lx35oVsY3W7qyRxbtkl5pjqBcRrIpeSE5KLdJheJ+/5dWef3NzepzYZn/baurQZ59xs6yx6KqtDHref/ufcvS2mlAIB8FCMrl46wxZgQQQAABBBBAAAEEEEAAAQQQQAABBBCwLeAbCs35ddU5FZ7zVdWLy5P+SXmrExrRkAy2rohXngt3qvCcCtFFt3VbdaUNAQQQQAABBBBAAIGiFPjU6c2qSpw59BaJxOT6O96Wri1BSxO32yU3XLGXVFf7Ta+HQjH56rffFL1UK1vuBTIF5d56d6v87s8bZc26fvnmZXtKnapClrwRlEvWmPrn++01XT50WIP8/sVN8tZ73bY/+6Uryn3jK3umLaE8MBiVy7/1xtRfGCNAYAIECMpNACKnQAABBBBAAAEEEEAAAQQQQAABBBBAAIE8EnC51DKtO6vQXCI4F688pyrRudweWxcRCw9IMB6eSwTn9LKtscE+W8fSCQEEEEAAAQQQQACBQhLQgbdbvr6PlJaa/y794t/a5PHfrMt4qbMbS+XqL+2u/g6e3uW+h1bKG0u2pL9Ay6QLJAfldGHtN97aIs+rsNX6TduX3SQoN+nTMKVvcOgBtfLJU5vTxnDdbW9JZ5d18DWtMw0IOFiAoJyDJ4ehIYAAAggggAACCCCAAAIIIIAAAggggEBuBHRIzl+/i8SrztU1xUN0nvIatXpUyvpRGYYTVUG5YOtyVXFue3jOCA9m6E0zAggggAACCCCAAAKFITCvqVwuu2DXtIu55Z53ZO2G7eGq5A4u9cGVK764qzTNLktuHnn+t9c75aEn1ozs8yR3Ah/5YKOc+JGd5B9vdKpqZK3S1pn+bxqCcrmbj6l4p8b6aWop5T3S3vqnD6+Uf6ngJBsC+S5AUC7fZ5DxI4AAAggggAACCCCAAAIIIIAAAggggMCkCLi8PgnUz48v16qrzukKdJ7SatvvpZdoDW5S4bmupPBcNGL7eDoigAACCCCAAAIIIOB0geOPmSXHHT3LNMzurSH5+s3/MbUl7xx5yEw548S5yU2m52Mdb+rMzoQKeFSFwMpKn2zpDmU8L0G5jDQF8YLX45K7b9w/7Vqe/O06+dNf29LaaUAg3wQIyuXbjDFeBBBAAAEEEEAAAQQQQAABBBBAAAEEEJgyAZevRAINCxLhuTq1dGtNk3hKym2PJ9LXKYO68lzHWgmr6nPhra1ihFm+xjYgHRFAAAEEEEAAAQQcJXDembvIPntON43p329vkZ8sXmlqG96prvLLNy7dUwIBizVXhzupx8u/9YYMDEaTWnjqFAGCck6ZickZh8/rlrtu2C/t5A8+tlpefaMrrZ0GBPJNgKBcvs0Y40UAAQQQQAABBBBAAAEEEEAAAQQQQAABRwm4A2USaFw4VHlOhedU9Tm3CtTZ3UJda7cv2drRIpGeNjGoPGeXj34IIIAAAggggAACUyjwtS/vITs1TjON4Ne/3yDP/3mTqW145/Nn7SJ772EO1g2/lvx46/fflZb1/clNPHeIAEE5h0zEJA1DL4l85YW7pZ39rp8sleWretPaaUAg3wQIyuXbjDFeBBBAAAEEEEAAAQQQQAABBBBAAAEEEHC8gF6iNVF5LhGc0+E5l8dra9yGYaiKc6tVeG4oQKeWbo30dIjEYraOpxMCCCCAAAIIIIAAArkQcKm0wR3f2k/8PnN1uPsfXSWvv7k5bQjv271azv/UfFP7i39rkyM/UC+ikwtJ2wOPrpbX3qR6VRKJY54SlHPMVEzKQE45drYcc0SD6dyhUEyuuvHfEgrzb1ITDDt5KUBQLi+njUEjgAACCCCAAAIIIIAAAggggAACCCCAQL4JeCtqVXhu/lDluSbxVjWoynMBW5dhxKISal81VHmuJR6ii/SpXxyqUB0bAggggAACCCCAAAJTIVBW6pVbr90n7a2tKk/ppVa/ccmeUl3tH+m/eUtIbrxridz01b1lWolnpF0/efq59fKHl1pNbew4Q4CgnDPmYTJGob+nv3nZXlJaav5+1MFXHYBlQ6AQBAjKFcIscg0IIIAAAggggAACCCCAAAIIIIAAAgggkJcCvpo54q9rFn9NovKcr2qmqjzns3UtRjQkg60rJDxceU49Rrd12zqWTggggAACCCCAAAII7KjAdBV6u/Gq96Wd5lu3L5H2zkFT+xknzJEjD1WV45K2e+5fJu8u65EbrnyfzJi+PUCnuzz3wkZ55g8bk3rz1CkCBOWcMhMTP46Pn9wkhx9cl3bi2+59V1avYynkNBga8lKAoFxeThuDRgABBBBAAAEEEEAAAQQQQAABBBBAAIGCFFDrV/nrdk4E5+qaVPW5ueKtqBOX2/yJ/kzXHgsPSFCF50Kdiapz+jE22JepO+0IIIAAAggggAACCIxboGFmiVx76Z5px3/9O29Kd094pL1pdplc8YXd1N9pR5rkH29slp8/lqhQ9dWLdpfZs0q3v6ievfDXNnnit+tMbew4Q4CgnDPmYaJHsev8SvnSZxeKXlI5eXvt35vlgV9QTS7ZhOf5LUBQLr/nj9EjgAACCCCAAAIIIIAAAggggAACCCCAQIEL6JCcf+a8oSVbVeU5FaDzlNeIS/1nZ4uqoFywdbmEutZKqEMH6FrECJsrfNg5D30QQAABBBBAAAEEEEgW0AG4Ky/cLbkp/vyqG/8tff2R+HO32xXvMycpCKdfu+HOJSN9Lj53oSzcpdJ0npdf7ZBHnm4xtbHjDAGCcs6Yh4kcRVWlT7725T2kvMxrOu22bVG56e4lpuCrqQM7COShAEG5PJw0howAAggggAACCCCAAAIIIIAAAggggAACxS3g8vokUD8/XnHOX9ssPrV0q7es2jaKXqI1UXlujQrOqQCdDs9FE7/MtH0SOiKAAAIIIIAAAggUtcAuzeXylfN3TTO44vp/y7aBxN8tP3RYvZx2/BxTn58/tlpVlOsaafv8WbvI3ntMH9nXT179V5c8+PhqUxs7zhAgKOeMeZioUXg9Lrn43EUyT30/mzZD5N4Hlsvby7aamtlBIN8FCMrl+wwyfgQQQAABBBBAAAEEEEAAAQQQQAABBBBAQAm4fCUSaNDhueahAJ2qPFeS8suOUaQifZ0yqCrPhTsSwbnw1lZVeS44yhG8hAACCCCAAAIIIFDMAs1z1JKqX0yvKHftLW/J5u6gTK/2y7WX7CmBwPY1V99d1iP33L/MxGYVlHvltU55+Mk1pn7sOEOAoJwz5mEiRqGXWf3Mx+bJ+/eekXa65/60UZ7548a0dhoQyHcBgnL5PoOMHwEEEEAAAQQQQAABBBBAAAEEEEAAAQQQyCDgDpRJoHHhSHDOXztX3L5pGXqnN8eXax2qOKcrz0V0eI7Kc+lQtCCAAAIIIIAAAkUo0Fg/Ta65ZI+0K7/5e+/Iuo3b5IKzF8heu1WNvB4KxeTGu96Wri3mD2NceM4C2X3h9n76gD+/0ia/fGbdyLE8cY4AQTnnzMWOjuTU42bL0Yc3pJ3mjSVb5KcPrxLDUGXl2BAoMAGCcgU2oVwOAggggAACCCCAAAIIIIAAAggggAACCCAwmoCntHqo8lzTyNKtLo93tENGXjOMWHyZ1lBHS2LJ1q41EunpEInFRvrwBAEEEEAAAQQQQKA4BGqmB+T6K/dKu9jv/XSZTCvxyLln7mJ67Yln18kLL7eZ2vTOJectkgXzKkztv/vzJvnN7zeY2thxhgBBOWfMw46O4pgjGuSUY2ennWblmj757k+XSiRCSC4Nh4aCECAoVxDTyEUggAACCCCAAAIIIIAAAggggAACCCCAAALjF/BW1CbCczVzxV/XLN6qBlV5LmDrhEYsIqH21YngXOea+GOkr0tU+QFbx9MJAQQQQAABBBBAID8FKsq8cvM1+6QNXi+ZevwxO0lVpW/ktZb1/XLbD95Tn69I/zvitZfuKQ0zS0b66ie/fn6DPP/iJlMbO84QICjnjHnYkVF84P21ctZpzWmn2Ng6IHf+6D3ZNhhNe40GBApFgKBcocwk14EAAggggAACCCCAAAIIIIAAAggggAACCEyggK9mjqo4p6vOJSrP+arqxeXZ/svO0d7KiIYk2LZS4pXnutbGH6Pbukc7hNcQQAABBBBAAAEE8kzA7XbJXTfsJx71mLzpz0u4kpp08eFb7nlH1m/altxt5Plt1+0br0A30qCeLH5ijfzf653JTTx3iABBOYdMxDiHsfce1XLeJ+eLy20+QdeWkAqzvis9vWHzC+whUGACBOUKbEK5HAQQQAABBBBAAAEEEEAAAQQQQAABBBBAYFIE1G87dbU5f83Qkq11TeKtqFO/YPHYertYeECCrSuGKs8llm6NDfbaOpZOfDQDHwAAQABJREFUCCCAAAIIIIAAAs4UuO6yPWVmrbkaXOpIf/9iq/zq+fWpzfH9koBHbv/mvmmv3fHD92RlS19aOw1TL0BQburnYLwjWLhLpVz4mQXi9SYlWdXJelU47nZVSa6jKzjeU3McAnkjQFAub6aKgSKAAAIIIIAAAggggAACCCCAAAIIIIAAAs4S0CE5/8x5quqcWrJ1qPKcp7xWVRAx/+Il06ijg32J8FxXIjgX6lgjRngwU3faEUAAAQQQQAABBBwmcMHZC2Sv3aoyjqqjc1BuuvsdCUdUWTmLbcG8CrnkvEVpr1x945vS209lqzQYBzQQlHPAJIxjCM1zyuTLn1skgYC5lNy2gajc+eP3RC+7yoZAMQgQlCuGWeYaEUAAAQQQQAABBBBAAAEEEEAAAQQQQACBHAm4vD4JzJyvqs/NFZ8Oz6kKdN6yatvvrpdoTVSe0+G5RIDOiPJLUtuAdEQAAQQQQAABBHIocMqxs+WYIxoyvuPdP1kqy1ZlriJ8zOENcspxs03Hb9sWlStueMPUxo5zBAjKOWcu7I6ksX6afOXzu0ppqbkaeCgUk+/et1RWr+u3eyr6IZD3AgTl8n4KuQAEEEAAAQQQQAABBBBAAAEEEEAAAQQQQMDZAi5fiQQaVHguXnUusXSrp6TC9qAjfZ2J8FxHIjgX3rpJVZ5jWSDbgHREAAEEEEAAAQQmSWC3hZXypXMWWp79b693ykNPrLF8bbhRV5PTVeWStzfe2iL3PbwyuYnnDhIgKOegybAxlNqagFx2/q5SWeEz9Y5EDLn3geWydGWPqZ0dBApdgKBcoc8w14cAAggggAACCCCAAAIIIIAAAggggAACCDhQwF1SLoF6XXlOV51TS7eqR7dvmq2RGmJIuGv9SMU5XXkusrVVjGjE1vF0QgABBBBAAAEEEJgYAZ/XLbd+Yx/x+8zLOepKVV//zpuybTCa8Y2qq/xy41XvE5dOLSRt//vLNfL3f3YmtfDUSQIE5Zw0G6OPpbrSJ5ddsJvMmO43dYyplZB/sniF/OfdblM7OwgUgwBBuWKYZa4RAQQQQAABBBBAAAEEEEAAAQQQQAABBBDIAwFPaXVS5TkVnlMV6Fwec+WDTJdhGLGh4JyqOtexVkJdKjzX0y6ifwvEhgACCCCAAAIIIDBpAhecPV/22q3adP5IJCbf+H9vydaesKk9eecTpzTJYQfWJTfF/+r2NRWw6+3LfJzpAHZyLkBQLufk43rDijKvXKoqydXXlZiONwyRB36xSl5/c7OpnR0EikWAoFyxzDTXiQACCCCAAAIIIIAAAggggAACCCCAAAII5KGAt6I2UXmudq74VHDOV92oKs8FbF2JEYtIqH21CtCp4JyqOhevPNfXJaJ/O8SGAAIIIIAAAgggMCECeulUvYRq6vbush754YPLJRJN/7vXbgsq5UK1ZGtqNblX/9UlDz6+OvVU7DtIgKCcgyYjw1CmlXji35OzZ5Wm9Xj4yTXyymtUbEyDoaFoBAjKFc1Uc6EIIIAAAggggAACCCCAAAIIIIAAAggggEBhCPhq5sSrzflVeE5XnfNV1duvPBcJSbB9ZVJ4bq1E+7cUBgxXgQACCCCAAAIITJHAV1Tlql2ay9Pe/Z1lW+XRp9dK15Zg/DW32yUH71cjHzt5rnjVsq3Jm/4sw/V3LJH2zsHkZp7nWEAvo3v04Q0Z3/WoQ2dKWanX9PobS7bIhk0DprbhneWremXFmt7hXR5zIHD+p+bL+3Y3V3nUbxsKx+SFl9vGNYLWtkF57U31oSM2BPJcgKBcnk8gw0cAAQQQQAABBBBAAAEEEEAAAQQQQAABBIpeQJUi8dc1i7+mSQXnEuE5b2WduNweWzSx8IAEW3V4LlF1Tlegiw3yyzxbeHRCAAEEEEAAAQSUwBxVueryL+yaFn7TOFFVUW7txm3i87ikotwnVZU+S7M//bVVnvztesvXaMydgF6y8+Zr9pmwN3zmDxvluRc2Ttj5ONHYAld9aXeZu1N6Nbmxj8zc4z9vd8uPFq/I3IFXEMgTAYJyeTJRDBMBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEDAvoAOyflnzhsJzukAnae8Vi3vpX8sPvYWHexT4bkVEurS4bm1ElYhuljIukrG2GejBwIIIIAAAgggUPgCB6lKcWefsfO4LnTpih655/7lEoulL9M6rhNy0LgFCMqNm84xBxKUc8xUMBAHChCUc+CkMCQEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBiRdweX0SmDlffLrqXJ2qPlejlvwqm277jaLbuiXYpsJzHYnwnK5AZ0TDto+nIwIIIIAAAgggUOgChx1YJ6efMEd8avlOu9u/394ii3+5RgYGo3YPod8kChCUm0TcHJ2aoFyOoHmbvBQgKJeX08agEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBCZCwOUrkUDDfFV5Tgfn1FedqjxXUmH71JG+zkTlOVV1Tgfowls3iREO2j6ejggggAACCCCAQKEJzKqfJqccN1t2m18lrlHycu2dg/LHl1rlldc6C40gr69nWolHblFLr3rUUrkTsT3yVIu8/I+OiTgV57Ap8MXPLJA9FlXZ7G2v28uvdsgjT7fY60wvBBwsQFDOwZPD0BBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQRyL+AuKZdA/VB4TlefUyE6t3+arYEYYki4a71arnWo6pxaujXS3aoqz0VsHU8nBBBAAAEEEECgUAQqK3yy165VMmN6QKrU847NQSkv9Up3T0hWt/TJqrX9hXKpXAcCCCCAQJ4IEJTLk4limAgggAACCCCAAAIIIIAAAggggAACCCCAAAJTJ+Aprd5eeW4oPOfy+GwNyDBiQ8G57Uu2RnraRWIxW8fTCQEEEEAAAQQQQAABBBBAAAEEdlyAoNyOG3IGBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgSIU8FbUxivP+YaCc77qRnH7ArYkjFhEQu2rVYBOLdk6VH1OL+MqhmHreDohgAACCCCAAAIIIIAAAggggEB2AgTlsvOiNwIIIIAAAggggAACCCCAAAIIIIAAAggggAACGQV8NXPEX6OWa61rii/Z6quqF9uV5yIhCbavMlWfi/ZvyfhevIAAAggggAACCCCAAAIIIIAAAvYFCMrZt6InAggggAACCCCAAAIIIIAAAggggAACCCCAAALZCbhc8cCcv7ZZPaoAXW2TeCvrxOX22DpPLDQgwbaVEupSy7Z2JJZujQ322jqWTggggAACCCCAAAIIIIAAAgggsF2AoNx2C54hgAACCCCAAAIIIIAAAggggAACCCCAAAIIIDDpAi63V/wzdx4JzukKdB61jKtLhersbNHBPhWeWzGyZGtYLd2qA3VsCCCAAAIIIIAAAggggAACCCCQWYCgXGYbXkEAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAICcCLq9fhefmDVWfG6o8Vzbd9ntHt3UPhefWJirPqQp0RiRs+3g6IoAAAggggAACCCCAAAIIIFDoAgTlCn2GuT4EEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBvBRw+Uok0DA/UXmuRi3dWqcqz5VU2L6WSG+HCs+tGqo81yLh7k1ihIO2j6cjAggggAACCCCAAAIIIIAAAoUkQFCukGaTa0EAAQQQQAABBBBAAAEEEEAAAQQQQAABBBAoaAF3SbkE6ndR4TkVnKtNVJ5z+6fZumZDDAlv3qAqzq1R4TlVeU5VnYt0t4oRjdg6nk4IIIAAAggggAACCCCAAAII5LMAQbl8nj3GjgACCCCAAAIIIIAAAggggAACCCCAAAIIIFD0Ap7S6kTluRoVnKtrEu/0WeLxl9pyMYzYUMU5FZzrVFXnVIAu3NMmEovZOp5OCCCAAAIIIIAAAggggAACCOSLAEG5fJkpxokAAggggAACCCCAAAIIIIAAAggggAACCCCAgE0BX3Wj+IYqzunKc77qWeL2BWwdbcQiiapzHS2q6pwK0KnHSF+niGHYOp5OCCCAAAIIIIAAAggggAACCDhRgKCcE2eFMSGAAAIIIIAAAggggAACCCCAAAIIIIAAAgggMMECvprZ4q9pSizZWtcsvqp6cXl8tt7FiIQk2L4qsWRrZ2Lp1mj/FlvH0gkBBBBAAAEEEEAAAQQQQAABJwgQlHPCLDAGBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQRyLeByJ0JztTo8lwjQeStnisvtsTWSWGhAgm0qPNeVCM7pynOxwV5bx9IJAQQQQAABBBBAAAEEEEAAgVwLEJTLtTjvhwACCCCAAAIIIIAAAggggAACCCCAAAIIIICAQwVcbq/4VbU5f91Q5TlVgc5TUSsul/51wthbdLBPhedWDFWea5Fw51qJhbaNfSA9EEAAAQQQQAABBBBAAAEEEJhkAYJykwzM6RFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQyGcBl9cv/pnzkqrPzRVv2QzblxTp75ZQ+0oVnmuJB+jCWzZILEh4zjYgHRFAAAEEEEAAAQQQQAABBCZEgKDchDByEgQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEikfA7Z+WCM/pynOq6pyuQOcpqbANEO5uHQnOhTrXSLh7kxjhoO3j6YgAAggggAACCCCAAAIIIIBAtgIE5bIVoz8CCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgikCbhLyiVQv4uqPKeXbW0SX+1c8fhL0/pZNRhiSHjzhkR4rkNVnutaKxEdnotGrLrThgACCCCAAAIIIIAAAggggEDWAgTlsibjAAQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEELAj4CmtTgrPqSVbZ+xkPzxnxOJLteolW8Oda+MhunBPm0gsZuet6YMAAggggAACCCCAAAIIIICASYCgnImDHQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEJhMAW91w1DVubmJynPVs8TtC9h6SyMWkVDHmpEAnQ7RRXo7RQzD1vF0QgABBBBAAAEEEEAAAQQQKF4BgnLFO/dcOQIIIIAAAggggAACCCCAAAIIIIAAAggggAACjhDw1cwWf41esnU4PNcgLo/P1tiMSEiC7atM4blo/xZbx9IJAQQQQAABBBBAAAEEEECgeAQIyhXPXHOlCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgjkh4DLrYJzKjRXtz08562cKS63x9b4Y6EBCbap8FxXS3zJ1pBaujU20GPrWDohgAACCCCAAAIIIIAAAggUpgBBucKcV64KAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIGCEnC5vSo415yoOqcDdCpI56moE5dL/6pj7C062Kcqz61US7e2SFgF5/SyrbHQtrEPpAcCCCCAAAIIIIAAAggggEBBCBCUK4hp5CIQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEECg+AZfXL/6Z8xLhOb10a91c8ZbNsA0R6e+WkA7PqdCcrjoX3rJBYkHCc7YB6YgAAggggAACCCCAAAII5JEAQbk8miyGigACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAqMLuP3ThsJzetnWxNKtnmmVox+U9Gq4u3UkOKeXbg1v2ShGOJjUg6cIIIAAAgggkK3A97/z/pFDLvzq6yPPeZJ/Asxl/s1ZphEzl5lkaC9kAYJyhTy7XBsCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgiIu6RcAvW7jATnfCpA5/GX2pIxxJDw5g3bw3Oq+lyke5MY0Yit4+mEAAIIIIAAAiIEcgrnLmAumcvCEeBKilGAoFwxzjrXjAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBAkQt4SqtN4TnvjJ3sh+eMWGKp1qElW/XSreGeNpFYrMhVuXwEEEAAAQSsBQhXWbvkYytzmY+zZj1m5tLahdbCFiAoV9jzy9UhgAACCCCAAAIIIIAAAggggAACCCCAAAIIIICATQFvdYP4a9RyrXVz49XnfNWzxO0L2DraiEUk1LEmHqDTwTn9FentFDEMW8fTCQEEEEAAgUIWIJBTOLPLXDKXhSPAlRSjAEG5Ypx1rhkBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQRsCfhmzB5ZstWvlmz1qTCdy+OzdawRCUmwfZWEutaqEJ0Oz62VaP9mW8fSCQEEEEAAgUISIFxVOLPJXDKXhSPAlRSjAEG5Ypx1rhkBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQTGJ+Byq6pzuuKc+qpTwTlVgc5XNVNcbo+t88VCA4nw3FDVOR2eiw302DqWTggggAACCOSrAOGqfJ259HEzl+km+drCXObrzDHuHREgKLcjehyLAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACRS/gcnvjoTldcS7xNVc8FXXiculfw4y9RQd74+G5sA7Pdajqc10tEgtuG/tAeiCAAAIIIJAnAgRy8mSibAyTubSBlCddmMs8mSiGOaECBOUmlJOTIYAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAIiLq9f/DPnJSrPqapzugKdt3yGbZpIf7eE2leq5VoTS7aGt2wgPGdbj44IIIAAAk4TIJDjtBkZ/3iYy/HbOe1I5tJpM8J4ciFAUC4XyrwHAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIFL2A2z9tKDyXCM7p6nOeaZW2XcJbW1XFuURwTledC2/ZKEY4aPt4OiKAAAIIIDBVAgRypkp+4t+XuZx406k6I3M5VfK871QKEJSbSn3eGwEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBIpawF1SIYF6VXmuZm58+VafDs/5S22ZGGJIePMGVXVOLdcarzzXIpHuTWJEI7aOpxMCCCCAAAK5EiCQkyvpyX8f5nLyjXP1DsxlrqR5HycJEJRz0mwwFgQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEECh6AU9pdSI8V9ucWLJ1xk72w3NGTMJd61TluTUS6lIBOlWBLtzTJhKLFb0rAAgggAACUydAIGfq7Cf6nZnLiRaduvMxl1NnzztPnQBBuamz550RQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAVsC3up6VXUuEZzz1zWJr3qWuH0BW8fqCnOhThWcG6k8t1YivR0ihmHreDohgAACCCCwowIEcnZU0DnHM5fOmYsdHQlzuaOCHJ+PAgTl8nHWGDMCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggUt4DLJb7pO8UrzvnVcq3+2rkqPNcoLo/PlosRCUmwY/XQkq2JynPR/s22jqUTAggggAAC2QoQyMlWzLn9mUvnzk22I2MusxWjfyEIEJQrhFnkGhBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABl1tVnZujQnOq4lzd3HgFOl/VTHG5PbZsYqEBCbavGqo8l6hAFxvosXUsnRBAAAEEEBhNgEDOaDr59RpzmV/zNdpomcvRdHitUAUIyhXqzHJdCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggUPQCLrdX9FKtuuKcvzaxdKunok5cqiKdnS062Cuhdl15bvvSrbHgNjuH0gcBBBBAAIERAQI5IxR5/4S5zPspHLkA5nKEgidFJEBQrogmm0tFAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBFxevwrP7ZwI0NXoAF2TeMtn2IaJ9Her8NzKocpzLRLeskEIz9nmoyMCCCBQlAIEcgpn2plL5rJwBLiSYhQgKFeMs841I4AAAggggAACCCCAAAIIIIAAAggggAACCCCAAAJJAm7/NPHPnDdUeS5Rgc4zrSqpx+hPw1tbE8G5jhYJda2Nh+eMcHD0g3gVAQQQQKBoBAhXFc5UM5fMZeEIcCXFKEBQrhhnnWtGAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBMYQcJdUSGCmqjynKs759NKtdc3i8ZeOcVTiZUMMCW/eqMJzieBcqGONRLo3iRGN2DqeTggggAAChSVAuKpw5pO5ZC4LR4ArKUYBgnLFOOtcMwIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCAwDgFPabUE6nXlOV11Ti3ZOmMn++G5WFSF59YnwnM6QNepKs+pSnQSi41jJByCAAIIIJBPAoSr8mm2Rh8rczm6Tz69ylzm02wx1okSICg3UZKcBwEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBAoQgFvdb34axLBOb+qPOebvpO4fQFbErrCXLzq3FBwTj+P9HaIGIat4+mEAAIIIJAfAgRy8mOe7IySubSjlB99mMv8mCdGObECBOUm1pOzIYAAAggggAACCCCAAAIIIIAAAggggAACCCCAAALFLeByia96llqqVYfn1JKteunW6kZxeXy2XGKRoOilWnXFOR2cC6vHSF+XrWPphAACCCDgTAECOc6cl/GMirkcj5ozj2EunTkvjGpyBQjKTa4vZ0cAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEHC5VdW5OeLTS7bWqfCcqkDnq6oXl9tjyyYWGpBgxyoVoEuE53SALjbQY+tYOiGAAAIITL0AgZypn4OJGgFzOVGSU38e5nLq54AR5F6AoFzuzXlHBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQKDoBVxu7/aqc3rpVlWBzlNRJy5Vkc7OFh3slVD7atPSrbFgv51D6YMAAgggkGMBAjk5Bp/Et2MuJxE3x6dmLnMMzts5QoCgnCOmgUEggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgi4vH4VmNt5ZMlWvXSrt7zGNkykv1tVndOV51ok1LVWwpvXSyy4zfbxdEQAAQQQmBwBAjmT4zoVZ2Uup0J9ct6TuZwcV87qbAGCcs6eH0aHAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCBS1gNs/Tfwz55nCc55pVbZNwlvbTFXnwls2iBEO2j6ejggggAACOy5AIGfHDZ1yBubSKTOx4+NgLnfckDPknwBBufybM0aMAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCBS1gLukQgIzdxZfrVqyVVWd86tHT6DMlokhhqo0tzERnlNV50KdLRLZslGMaMTW8XRCAAEEEMhegEBO9mZOPYK5dOrMZD8u5jJ7M47IfwGCcvk/h1wBAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIFD0Ap7SahWeU5Xn6ppUgG6u+GbMFo+/1JaLEYvGl2nVoblQZyI8F97aKhKL2TqeTggggAACowsQyBndJ59eZS7zabZGHytzOboPrxamAEG5wpxXrgoBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQKHoBb1V9vNqcrjinK8/5pu8kbl/AlouuMBcPzumqcx06QKcqz/V2iBiGrePphAACCCCwXYBAznaLfH/GXOb7DG4fP3O53YJnxSNAUK545porRQABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQSKW8DlEl/1rER4rk4t2Vqjqs9NbxSXx2fLJRYJqtDcmnjVufBQ9blIX5etY+mEAAIIFLMAgZzCmX3mkrksHAGupBgFCMoV46xzzQgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgkBl1t8NXOGKs+p8JyqPudTlehcbo8toWhomwrPrU4s2aorz6kKdLFtW20dSycEEECgWAQIVxXOTDOXzGXhCHAlxShAUK4YZ51rRgABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQyCjgcnvFX5dYrjVedU6F57yVdeJSFensbNHBXgm16/CcXrJVLd2qHmPBfjuH0gcBBBAoSAHCVYUzrcwlc1k4AlxJMQoQlCvGWeeaEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEshJwef0qPNc8VHkuEaLzltfYPkekf0ui8txQ1bnw5vUqPLfN9vF0RAABBPJZgHBVPs+eeezMpdkjn/eYy3yePcY+XgGCcuOV4zgEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIGiFnD7p4l/5jzx16glW4cq0HmmVdk2CW9tG6o61yJhXXluywYxwkHbx9MRAQQQyBcBAjn5MlNjj5O5HNsoX3owl/kyU4xzIgUIyk2kJudCAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBAoagF3SYUKz+08VHlOBejUsq2eQJktE0MMCW/ZqCrPqSVbuxJLtkbUvhGN2DqeTggggIBTBQjkOHVmsh8Xc5m9mVOPYC6dOjOMazIFCMpNpi7nRgABBBBAAIGMAr6KMpm++yIp26lRSmbWiq+iXLwlJeLyusWIxCQy+P/Zuw/4KKq1DeDvtiQQ0hNCJ0jvCFgQAQsgomDlwwJiQZGmKFexIVgQAfVKkWbBDgKKAiLSBfSqSO+9JQFCQkhvm80374SN2d2Z3ZnN9n3O/eVu5syZMzP/CQGTZ99TSCU5uVSYlk55Keco88BhYTtPdj7sgAAEIAABCEAAAhCAAAQgAAEIQAACEICArwrowmMoNKERGeLLK88ZYuuRLqS6osstM5USL9NazBXn0oUAnfBRknWeyGRSdDwGQQACEPAFAQRyfOEpuOYa8Cxd4+gLs+BZ+sJTwDV4WgBBOU+L43wQgAAEIACBIBcIjYmiOrd2o5jWLUir0ynWMJWWUub+Q5S6fgsVZWYpPg4DIQABCEAAAhCAAAQgAAEIQAACEIAABCDgiwL6qESh2hxXnEsSXw0xdUlrCFV0qVxhzlxxTgzPCRXojDkXicrKFB2PQRCAAAQ8LYBAjqfF3Xc+PEv32Xp6ZjxLT4vjfL4ggKCcLzwFXAMEIAABCEAgSAQSb7iG6vbqoSogZ01jMhopZd1muvDHNutd2IYABCAAAQhAAAIQgAAEIAABCEAAAhCAgP8KaDRkiK5dEZzjJVsNMbVJozMouieTsUhYsvWUWHmuRKw8d4aMuRmKjsUgCEAAAu4WQCDH3cKemx/P0nPW7j4TnqW7hTG/LwogKOeLTwXXBAEIQAACEAgwAY1WS0n39qW4dq1ddmcZe/bTqR9WURmWmHCZKSaCAAQgAAEIQAACEIAABCAAAQhAAAIQ8DEBjZYMcfWvVJ5rWF55LqoWabTKVmooLc6/Ep7jJVvLl2415WO1Bh97yrgcCASFAAI5gfOY8SzxLANHAHcSjAIIygXjU8c9QwACEIAABDws0Oj+O10akjNfPoflTi5dad7EKwQgAAEIQAACEIAABCAAAQhAAAIQgAAEAl5Ao9WXB+cShIpzceVLt+ojE0gjVKRT0koLc6g47WRFcI6XbjUV5Sk5FGMgAAEIOC2AcJXTdD53IJ6lzz0Spy8Iz9JpOhzoxwIIyvnxw8OlQwACEIAABPxBgJdbrd/nFrdd6tnVG7AMq9t0MTEEIAABCEAAAhCAAAQgAAEIQAACEICAPwho9KEUIgTnQjg4l5AkBun0NeIUX7oxL7O88hwv3ZpxhkouJQvhuXzFx2MgBCAAAUcCCOQ4EvKf/XiW/vOsHF0pnqUjIewPRAEE5QLxqeKeIAABCEAAAj4iEBoTRa2feZK0OmVLQThz2SajkfbP/ISKMrFkhDN+OAYCEIAABCAAAQhAAAIQgAAEIAABCEAgMAW0IdWE0FwjITRXvmQrB+l01aIU32xJVppQde4UlQjBueKLwtKtmSlUVlKk+HgMhAAEIFBZAIGcyhr+/TmepX8/v8pXj2dZWQOfB4sAgnLB8qRxnxCAAAQgAAEvCLhryVXrW8ESrNYi2IYABCAAAQhAAAIQgAAEIAABCEAAAhCAgK2ANiyCQmpyeI6XbC2vQKcLq2E7UKKnrKyMSi6nVlqy9QwZOTxXapQYjS4IQAAClgII5Fh6+PMWnqU/Pz3La8eztPTAVnAIICgXHM8ZdwkBCEAAAhDwuIChRji1HTvcrdXkzDdlKi2lve/PoZLcPHMXXiEAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEFArrwGAoVlms1XKk8Z4irT7qQ6gqOJCozlQrLtKYI4Tmh4ly6UHmOK9BlnScymRQdj0EQgEDwCCCQEzjPGs8SzzJwBHAnwSiAoFwwPnXcMwQgAAEIQMADAvGd2lHSXbd74Ezlpzj10y+Uvn2Px86HE0EAAhCAAAQgAAEIQAACEIAABCAAAQhAIFAF9JE1hWVbecnW8g9DTF3SGkIV3S5XmCvm5VqvBOf41ZidJqTqyhQdj0EQgEBgCiBcFTjPFc8SzzJwBHAnwSiAoFwwPnXcMwQgAAEIQMADAkl39aH4Tu09cKbyU6Rv302nflrtsfPhRBCAAAQgAAEIQAACEIAABCAAAQhAAAIQCBoBjYYM0bUrgnO8dKshpg5pdAZFBCZjUXnVuYunqUQM0J0mY26GomMxCAIQ8C+BO3vVoWphOpuLvumGxIq+TX9cqPjc/ElBYSmtXJtq3sSrDwjgWfrAQ3DRJeBZuggS0wSEAIJyAfEYcRMQgAAEIAAB3xNo9tiDFNmogccuLPvkGTqyYKHHzocTQQACEIAABCAAAQhAAAIQgAAEIAABCEAgqAU0WjLE1SsPz8U1ECvQGaJqkUZrG5CRciotzqfii6euVJ4rD9CV5l+WGoo+CEDAjwTu7FmHbr+1juor/mV9Kq1ch6Ccajg3HoBn6UZcD0+NZ+lhcJzOpwUQlPPpx4OLgwAEIAABfxd4+OGHKSQkxOI2tm3bRvv27bPoc+eGXq+nUaNGqT7F7t27aePGjaqPMx/QevRQqpYQZ960ea1WrRo1a97Mpt9RR0pyCqWnp9sMK7iYQftnfmLTr7ajadOmdMcdd6g9jJYtW0anT59WfRwOgAAEIKBEoH///nTVVVcpGVoxpqCggObNm1exjU8gAAEIQAACEIAABCAAAQhAAALuFtDo9BQihOYMQsU587KtvIyrRqhIp6SVFuaUh+eEynPi8q1CkM5UlKfkUIyBAAR8RKB6NR299WI7CpOoKid3iYVCNbnxU/dQfkGp3BD0e0EAz9IL6G46JZ6lm2AxrV8KICjnl48NFw0BCEAAAv4gMHDgQFq0aJHNpU6cOJHeeOMNm353dVSvXp3y8tT/MOmjjz5yKmBnvo92L46ikBrh5k2b1/j4eHrgwQds+h11bNm8hTjEZ92Kc/Noz9RZ1t2qt//v//6PvvvuO9XH3X777bR6NZZ+VQ2HAyAAAUUCy5cvp379+ikaax7EoeKEhATzJl4hAAEIQAACEIAABCAAAQhAAAJeEdDoQ8VqcxygC0loKAbo9DXk32BrfZHGvMwrleeE8Fy6UHnuUrIQnsu3HoZtCEDAhwTUVq9CNTkfenhWl4JnaQXix5t4ln788HDpLhVAUM6lnJgMAhCAAAQgUC4QFhZGBw8epKSkJAuS7OxsatSoEV26dMmi350b3grKXf3qGNKFhsremquDcqVFRbRz0oey51O6A0E5pVIYBwEIeFIAQTlPauNcEIAABCAAAQhAAAIQgAAEIOBuAW1IdSE0lySE5syV5xqQrnq04tOWZKVRiRCaK87g8NwZKhbCc2UlRYqPx0AIQMC9AmqqV6GanHufRVVnx7OsqqDvHI9n6TvPAlfiXQEE5bzrj7NDAAIQgECACrz00ks0efJkm7vjSnJcUc6TzVtBuY6vjyWtsOyrXHN1UM5kNNKON9+XO53ifgTlFFNhIAQg4EEBBOU8iI1TQQACEIAABCAAAQhAAAIQgIBXBLRhEeXhObHqnBCgi2tIurAaiq6lrKyMSi6fEyvOicE5IURnzEyhslKjouMxCAIQcL2A0upVqCbnentXz4hn6WpR782HZ+k9e5zZdwQQlPOdZ4ErgQAEIACBABGoWbMmHTt2jCIiIizuKDMzU6wml5WVZdHv7g1ng3KzZs2i0aNHO315HSe+QFqtVvZ4Z4Nymzdvpj2799jMazKZaMfEaTb9ajucDcr16dOHfv31V7Wnw/ggEYiOjhYrTMbGxlJOTo5YVZIrS/L3BTT/EODv6bVr16bExETxe9vly5fF58fLm+bnu3+5FwTl/OPrBFcJAQhAAAIQgAAEIAABCEAAAq4V0IXHUKhQec4Qz0u2NiBDXH3SCdXolLQyU6mwTGtKedW5i0LVOV62NesckfBzRDQIQMD9AkqqV6GanPufgyvOgGfpCkXfmAPP0jeeA67CuwIIynnXH2eHAAQgAIEAFJg3bx499dRTNnf22muv0aRJk2z63d1hLyi3adMm2r59u+QlzJkzh44fPy65T0lnpzdeJI2G/6kh3ewF5Q4cOEBFwlKq1o3fGcrXW1Qos2/CVOtDVG/bC8q9/750xToOPnEFweLiYtXnU3pAZGQkdenSRdKUrTZu3Kh0Koxzo4BeqKJ422230S233CIGY3mp5aSkJOKgnFRLTU2l33//nTgAumjRIuLQFZr3BPh7VosWLahz587UqVMn6tChA9WvX59q1apF/L1UqvH3Jf6e9b///Y/WrVtHy5Ytc8v3gscff5xatWoldQnUrFkz6tevn80+/npKSEiw6UcHBCAAAQhAAAIQgAAEIAABCEDAnwX0kTWF0JwQnOPKc3FCeC62HmkNoYpuiSvMFWdwaK48OMfhOWN2GpHw3/doEICA6wUcVa9CNTnXm7trRjxLd8l6fl48S8+b44y+JYCgnG89D1wNBCAAAQj4uUDbtm1p586dpNPpLO4kIyNDDM1woMrTzV5Q7tprr6Vt27a55ZI6vznO7rz2gnLz5813Kmjyz+tT7J5TyU65oFxaWppYSUrJHK4cwyGXZ599lkaOHCkbtrpw4YIY5HHleTGXOoGOHTvSI488Qg8++CBxVUlnWkFBAX399dc0ZcqUKoVUnTl3MB/D369HjRpF3bt3Fz/4e1NVGv95/Pjjj2nq1Kli9cCqzKX02Pvvv5+WLFliMxxBORsSdEAAAhCAAAQgAAEIQAACEIBAIAoIb3wzRNcuD88JVec4RGeIqUManUHR3ZqMReXV5jg8d/G0GKQz5uDNjIrwMAgCDgTsVa9CNTkHeD62G8/Sxx5IFS4Hz7IKeDg0IAQQlAuIx4ibCDYBXrbt+++/l7ztwYMHU3JysuQ+dEIAAu4X4KU3e/fubXOicePGiaEJmx0e6EBQTh2yrwTluIrV2LFj6cknn5StZGW+MwTlzBKefeU/W7w8MQfk5Cp9OXNFvIznc889R/Pnz3fmcByjUqBGjRpuCbSdOnWKuAqcJ6o9Iiin8qFjOAQgAAEIQAACEIAABCAAAQgEvoBGKyzTWq88PCdUnRPDc9G1SKO1fIOzHERpcb4QmjslVp4rEarOcQW60vzLcsPRDwEI2BGQq16FanJ20Hx0F56ljz4YJy4Lz9IJNBwSMAIIygXMo8SNBJNA7dq1iZdqk2rNmzenI0eOSO1CHwQg4GaBdu3a0e7du23OkpubK1b7ysvLs9nniQ4E5dQpezsox0socrCSg88Gg7J3vSIop+4Zu2p0y5YtxSU3XTWf9Tzffvut+HVgMpmsd2HbhQLuCsrxJfKyrE8//bTbQ48IyrnwCwJTQQACEIAABCAAAQhAAAIQgEDACmh0eiE8x6G58uAch+d4GVeNUJFOSSstzLkSnisPzvGyrabCXCWHYgwEglpAqnoVqsn555cEnqV/Pjepq8azlFJBX7AIICgXLE8a9xlQAgjKBdTjxM0EkMDs2bNp+PDhNnf05Zdf0pAhQ2z6PdWBoJw6aW8F5Tp06EAvv/wyceBFq9WqumgE5VRxuWywu4NyfKGffvopDR061GXXjIlsBdwZlOOzcVju0UcfJf67wF0NQTl3yWJeCEAAAhCAAAQgAAEIQAACEAh0AY0+lEISGlIIB+j4VQjR6WvEK75tY16mRXiu5NJZMhXlKz4eAyEQLALW1atQTc5/nzyepf8+O+srx7O0FsF2sAggKBcsTxr3GVACCMoF1OPEzQSIAActuNJjRESEzR316tWL1q1bZ9PvqQ4E5dRJezood+ONN4oBub59+6q70EqjEZSrhOHBT5UG5S5evEiHDh0ifk68rGpSUhJxBdjExERFV/v888/Tf//7X0VjMUi9gJKgXGFhIaWkpIjL2/OrXq8Xn2OjRo0oISHB4UmLioqoTZs2dOzYMYdjnRmAoJwzajgGAhCAAAQgAAEIQAACEIAABCAgLaANqX4lNMfBufLwnK56tPRgiV5jdpoQnhOqzmVcqTx3KZnKSookRqILAsEjULl6FarJ+fdzx7P07+dX+erxLCtr4PNgEkBQLpieNu41YAQQlAuYR4kbCSCBYcOG0dy5c23uiMNz9evXJ28unYignM1jsdvhyaDcwIEDadGiRXavR8lOBOWUKLl+jL2g3KZNm+iHH36gjRs30v79+8WqYtZXwAGr119/nUaMGGG3imB2djbxkrz8nNFcLyAVlNuzZw+tWLGCVq1aRYcPH6aMjAzZE3NYbsKECTRo0CDS6XSy43iuO+64Q3Z/VXYgKFcVPRzLSwxVr1ebQmOiKCQykjR6HZXk5FJxVjblp5wnY0EhkCAAAQhAAAIQgAAEIAABCAS9gDYsQgjPJZUH5xKE6nNCBTqd0KekcbX5ksvnyoNzF89QibBka0lmCpWVGpUcjjEQCBgBc/UqVJPz/0eKZ+n/z9B8B3iWZgm8BpMAgnLB9LRxrwEjgKBcwDxK3EgACezatYvat29vc0fvvfcevfDCCzb9nuxAUE6dtieDcrwc44IFCxRd4LZt2+iaa66RHIugnCSL2zutg3KZmZni85w/f74YrlJ6AR07dqTPP/+c2rZtK3vIzJkz6ZlnnpHdjx3OCxgMBtqxY4dYFZTDcfxx+vRp1RO2atWKfvrpJ2rSpInssVxVjoOTrm4IyrlaNDjm01evRrW7d6G4jm2FgJz0L3fKSksp88AROr/lT8o9kxIcMLhLCEAAAhCAAAQgAAEIQAACCgX0NeLIICzVaq46Z4irTzqhGp2SVmYqFcNyxUJorjj9jFiBriTrHAnvuFZyOMZAwC8FuHrVK8+0pndm7Kf8glK/vAdcdLkAnmXgfCXgWQbOs8SdKBdAUE65FUZCwGcEEJTzmUeBC4GAKHD99dfT//73P0kNDs9xZSJvNgTl1On7WlBu8+bN9Pbbb9PatWtp37591Lp1a5sbQlDOhsQjHeagXEFBAU2fPp2mTJlCly9fdurcHK7i7xXVqlWTPP7cuXNUt25dycp0kgeg0ysCnTt3pj/++IM4fCfVxo8fL/55ltpXlT4E5aqiF5zHRiTVp8YP3kMhUZGKAMpMZZSyfjOd27AV34cUiWEQBCAAAQhAAAIQgAAEIBCsAvrImhXBOQ7QGWLrkdYQqoiDK8wVZwihOQ7OcYBOWLrVmJVGwn+IKToegyDgDwLxsaGUfglLEfvDs3J0jXiWjoT8Zz+epf88K1ypawQQlHONI2aBgEcFEJTzKDdOBgGHAlwJasiQITbjOPQiVWXOZqCbOxCUUwfsK0G51atX06RJk2jr1q0VN4CgXAWFT3zCYahnn32Wvv32W7EaWVUvauzYscRVKOUah3L/+usvud3o9xGBl19+md555x3Jq+E/z926dZPcV5VOBOWqohd8x0Y0akAtnhxEGq1W9c2n/bWDTi1bpfo4HAABCEAAAhCAAAQgAAEIQCBoBTQaMkTXFsJz5spzQngupg5pdNJvsrN2MhmLhKVarwTnrlSfM+akWw/DNgQgAAEIQAACEICAQgEE5RRCYRgEfEkAQTlfehq4lmAX0Ag/6EhLS6P4+HgbijfffJMmTJhg0+/pDgTl1Il7MyhXJrw79McffxQDctu3b7e5cATlbEgCqkOn09HZs2eJ/56Xak8//TTNmzdPahf6fEigadOmdOTIEckrOnXqFDVq1EhyX1U6EZSril5wHcsV5FqPfoIMNcIlb7y0qFj4ZY2WtHq95H7u5KAcB+bQIAABCEAAAhCAAAQgAAEIQMBJAY1WqDRXl0ISkigkrjxAZ4iuJbyhSadowtLifGGp1lOVAnRnqDTfuVUOFJ0QgyAAAQhAAAIQgEAACSAoF0APE7cSPAK+HpSLiYmhPn36UKdOnahmzZqUmJgohoiKi4vFQBEvEci/QF65ciUdOnTIKw/u5ptvpu7du1ODBg2ofv36lJCQQNnZ2cTXduzYMfHa/vzzTzKZTG69Pl5qj3+h3rBhQ/EjKSlJvKbQ0FDRipfaO3/+PPFramoqbdmyhS5evOjWa+LJ27VrR7fffrv4y3x+hvyRk5NDISEh4nVxMGzHjh30yy+/iNtuvyAVJ+BlC/v16ye6chiBP/hrMiUlhU6cOEHHjx+nAwcO0Pr1612ydBgvg8nhJanWt29f0Uhqnyf7EJRTp+2NoFxpaSktWrSIJk+eTPv375e9YATlZGkCZgdXErztttsk72fixIn0xhtvSO5Dp+8IaIUqXfx3Jn/vtW6FhYWyy+taj1WzjaCcGq3gHtvk4fsotm1LG4S8lPN05ue1lHPitBCU01FEo/oU37G98NHWZqypxEi7p8ykktw8m33ogAAEIAABCEAAAhCAAAQgAAHnBDQ6PRk4NMcfCQ3FCnT6yETiN2oraaWFOWJ4TlyyVaxAd4ZMQh8aBCAAAQhAAAIQgIClAIJylh7YgoDXBXr37k2rVjlezoirzkg1DluoaSUlJS75hS3/x9pDDz1Ew4YNoxtuuIHkrs/62jiU9uGHH9LcuXNJ7bVXnosr7DzxxBOVu8TPv/nmm4olMTnkNXLkSOKKPM2aNbMZa93BoSpeUo8Dfa5uXbt2FSuN9erVS9XUHNzjAN/y5cvFj4MHD6o63t7giIgIGjduHD3yyCNieNDeWPM+rn71999/0/vvv09Lliwxd7v0lYOCUtXa+Dp5uUNz47AjP1/+kBpvHmd+5ZDm1KlT6euvvyb+c+BsGzFiBH300UeSh8fFxdGlS5ck93myE0E5ddqeDMpxSPb555+nGTNmiCFOR1eKoJwjIf/f/8EHH9Bzzz0neSOzZ88Wv8dJ7kSnTwnw3zHNmze3uSb+e1MvVOpydRAfQTkbanRICFSrGU9tn3/aZk/OqbN0eMFCMgnV5Kxbo/vvpITOHay76dzm/9HZVett+tEBAQhAAAIQgAAEIAABCEAAAq4T0OhDKy3ZWh6g09ewXdlE7ozG3EskBucyhKVbL56mkktnhf/2y5cbjn4IQAACEIAABCAQFAIIygXFY8ZN+pMAV5HhajKeakajkQwGQ5VO16NHDzEoxRXknG1cQYkDbFu3bnVqik8++UQyKMchqMGDB4tV7ZYuXUo33nij6vmnT59OY8aMUX2c1AEckONqQLfeeqvUbtV9ixcvpoEDB6o+zvqAIUOG0Lvvvku1atWy3qV4e/PmzTR69Gjas2eP4mOUDOQKelLBt0GDBhEHIbndc889YuBNqnqPo3MkJyeLVef4z4IzjZ/BgAEDbA49evSookCmzYFu6EBQTh2qJ4Ny6q6MxOqFXMXQunE1zKr8+bWeD9veE+CANgfIpdqsWbPE77NS+9DnOwL8ZgGuUiv1d9KZM2fECrKuvloE5VwtGpjzXTWgH8V3am9xcybh3z97359LRZnSS/RohAqJ7ceNIl6ytXIzFZfQzkkfUmlRUeVufA4BCEAAAhCAAAQgAAEIQAACbhbQhlSvqDgXEp8kVqHTh0crPqsxO00IzwnBufRT5a8ZyVRmxH/bKQbEQAhAAAIQgAAE/F4AQTm/f4S4gUAT8Leg3Msvv0zvvPOOSx4DL836wAMP0LJly1TPZy8o9/bbb9PatWsVV0mTOjnPMX78eKldivv42f7888+Kq+0pmZiXD+3Zs6eSobJjOCDHleRc0XJzc+nOO++k3377zRXTiXM4Cso9+uijxM9faRVDqQvjpW7568+ZxhXveHlh62YOaVr3e2MbQTl16gjKqfPCaNcKTJgwgXiJVan20ksv0ZQpU6R2oc+HBNq0aUN79+6VvCL+9whXD3Z1Q1DO1aKBN59Gq6Grxz9PemGJ+srtwu/b6PSKXyt3WXxevU4itR41lPh463b066WUue+QdTe2IQABCEAAAhCAAAQgAAEIQMDDAtqwiCvhuaQrFegakE7oU9K4+n3J5fNUnHGKSi5ygE6oPJeZQmWlzr2xXMk5MQYCEIAABCAAAQh4UwBBOW/q49wQkBDwl6CcVqguwZXWRo0aJXEXznfx8qtcKWzRokWqJpELyn333XfUpEkTqkq1O74QXiKN59i1a5eq6zIP5l+a//777xQZaVmNw7zf2deqBuW4ahFXL3JlKygooP79+9O6detcMq29oBwvp/vpp58SL/1bleZsUK5FixYkt/wt/9mQW5K1KtfqzLEIyqlTQ1BOnRdGu1bgyy+/FCuhSs3KS5wvXLhQahf6fEjgvffeo7Fjx0pekbuqAiIoJ8mNzkoCNRrWo1bDH63UU/7p/pmfUl7KOZt+7uB/X7Ua+RiF16sjuf/itl108vuVkvvQCQEIQAACEIAABCAAAQhAAALeFdCFx1aqPNdQqDxXn3RCNTolrcxUKoblyivPnRYrz5VcTuVflCg5HGMgAAEIQAACEICATwsgKOfTjwcXF4wCng7K7dy5kzp27KiaeurUqfTCCy84PI6Xszx8+DAdP35cXBaQlwwMDw+3e1x+fr4YSjt0SHmFCrmgnL0T8Xn4umrXri25tKf1sRs2bHBqyVQOFR45coQaN25sPaXFNl/LyZMniSuU5eTkUJ06dcSQHx8XFhZmMda8UZWgnFwYyDy3+ZWfA18/V4tLSEgQv17i4uLMuyVfMzIyqGXLlsQht6o2uaDcM888Iy5jGxMTU9VTkLNBuSeffJLmz58vef5rrrmG/vnnH8l9nu5EUE6duNyfjbS0NMnqgepmr9roffv2EZZerZqhLx/NS6GfO3eO5L7H8rM/cOCAL99C0F+b3PcPM8zNN99MmzZtMm+67BVBOZdRBuxEdXt2J/6o3IqzsmnX5BmVuyw+T7zhGmrY/zaLvsobjo6vPBafQwACEIAABCAAAQhAAAIQgID3BfSRNa9UnGsovhpi65PWEKrowrjCXPGls1R8sTw4xxXojFlpREJFOjQIQAACEIAABCDgTwIIyvnT08K1Bo2Ao+pYHOxKSUmR9OAKVxxqUtq4rLbaxkuGrV692m4Vr5UrVxIvH7d//34qKiqqOAWHxho1aiSG7IYNG1bRb/0JB/iuv/56xcthKg3KZWdnEy9dt2XLFrESGFew48ahtIEDB4pL2nFQQa4lJSXR6dOn5XZL9nfr1o02b94suY/9FyxYIIat/vrrL8kx/PVQr1496tKli1jBj+czN2eDcrGxsWLYQmrJUPPcixcvpkmTJtGePXvMXeIr+/Tp04emTZtGzZs3t9hXeYOrHnH1o6o2uaAch5Zq1qxpMz0/n59++kn8GuXwIYf2+H75WvnPB1f34yBDtUpLjzkblGOfV155xeYauIMDoRzG9IWGoJy6pyAXdEFQTp0jRqsX6Nu3r7hEt9SRHJJs27at1C70+YAA//uGw2qfffaZ7BsCVqxYIVZcdcflIijnDtXAmrPpoPsppk0Li5viZVN5+VSpFhIVSW2ff5p0oSFSuyv6tk+cRqWF//5bv2IHPoEABCAAAQhAAAIQgAAEIAAB3xcQfvdgiKpVXnkuroHwmkSGmDqk0cn/jqTyTZmMRVSSzsu18scp8dWYk155CKq1mhkAAEAASURBVD6HAAQgAAEIQAACPieAoJzPPRJcEAQcC3BQLjVVKHMt0TgMpCYoJzGF3a74+HjiX9bLBayysrJozJgx9Pnnn9udh3fefffdxAE3uco5o0ePJl6iTElTEpQ7evSo+Atqe5XqOIz2/fffi1XmpM774osvigExqX1yfbxELVc/k2pclY+XaFPTuAIgL5f6wAMP0Jo1a6hfv35qDhfHzpgxg9hXqnF4b+jQoeIv+6X2m/s4aMbPmUNFcu2mm26i3377TW63on65oJz1wZcuXaKJEyfS3LlzqaSkxHq3xTYHDznkNnjwYDHw6WxQjqvJcVU561ZYWGgRxLPe7+ltBOXUiSMop84Lo10jwKFoXqKb/x6Saq+++iq98847UrvQ50WBiIgI8e9hfj6tWrWSvRKusMtBR3v/BpE9WMEOBOUUIF0Zoq8WRok3Xif+/Z+8ZpPyA/18ZJsxT1H1WpZvMEj+dSOlbvxd8s6aDh5AMa3l3xBhPmj/LGHp1uRz5k28QgACEIAABCAAAQhAAAIQgIC/CwhvBjTE1BUqznHVuSuV56Jrk0arU3RnpcX5QnhOqDrHlecyhACd8Fqaf1nRsRgEAQhAAAIQgAAEPCGAoJwnlHEOCLhYwJtBubfffpv4l8FSjQNNvNykmoprzZo1o927d0suLXrmzBlx6VFHoSe+FkdBuV27dhEvd3b5suP/IOPw1Jdffil1i7Rq1Sq64447JPfJdfJ91K9f32a3M3NVnoTDilzBhpfpU9M44MbHREVFSR7GFdImT54suc+6kwNmXNWua9eu1rvEbVdUlVMSlGPjXr16qQ6Jcujw0UcfFcOdJpNJ8h7sdf74449011132QzhIGvdunVt+r3VgaCcOnkE5dR5YbRrBB555BH64osvJCfLy8ujpk2bqv5+LzkZOlUJcADtnnvusTiGv6c2bNhQ/OCKpY4aV7Pl7yu//vqro6FO70dQzjGdOSBXq+u1pAsLpQu/b6PTK9z3TBxfkQdHCEHczm++KCynY7A46fGFyyhj936LPt6IadWMmj5i+UYI9krseo3N2OOLfqSMXfts+tEBAQhAAAIQgAAEIAABCEAAAoEjoNHpyRBXn0LiyoNzIQkNSR+ZaHfVocp3X1qYIwTmyivOFXOITqhAZxL60CAAAQhAAAIQgIA3BBCU84Y6zgmBKgp4KyjHvxjmQJJcBbj+/fsTLyumtnHwjgN4Uo1Da19//bXULos+R0E5DjMtX77c4hi5jZCQEDHsV6tWLZshfP/8y3GlTafTidXNpJbTdaY6ndLz2hv38MMPy5pyaPHqq68mNUvyctjxwIEDxPdq3biyGn+9KgkoWh9r3nYUlCsoKBCr+Jw6dcp8iMdeufrTDTfcYHM+X1siEUE5m0dktwNBObs82OkGgZiYGPH7qNTfO3y61157TayC6YZTY0oHAm+++SaNHz/ewSj53fzmAa78unfvXvlBLtiDoJw8onVAzjwymIJy+urVqOPrY823XvF6cP5XlHPidMU2f6IVllptJyy5ykuvmltRZhbt/e9cuvqVMWLI0NzPr2d/2UDnfvujchc+hwAEIAABCEAAAhCAAAQgAIEgENDoQ4WKc8JyreJHeYBOH5Gg+M6NuZfKl2zNKA/O8RKuJqEaHRoEIAABCEAAAhBwtwCCcu4WxvwQcIOAt4Jyw4cPp9mzZ0ve0Zw5c2jEiBGS+xx1cjCNK761bNnSZujSpUtpwIABNv3WHfaCchziatOmjarwl71fjEdGRlJOjrJ3O3H4gZcElWpcPeirr76S2uXWvl9++YX69OkjeQ41gcLKE/ASrEOGDKncVfE5L03Kz8fZ5igoN23aNOLQoTcaL3PMVZ6sGy83y8vO+kpDUE7dk0BQTp0XRldNgIPUP/30k+wy2idPnhTDwBw8RvO8gL1/D9i7mq1bt4r/ZuLl3IuLi+0Ndck+BOVsGeUCcuaRwRSUC42OpPYvPWO+9YrXPe/NocL0jIpt/qRhv95C5bhrLfoOf7aQso4cp/bjRlNojGVF4tQNWymYlrC1gMEGBCAAAQhAAAIQgAAEIAABCFgIaEOqly/XmtCADLxsq1CBTh8ebTHG3oYxO608PCdWneOlW5OpzFhk7xDsgwAEIAABCEAAAqoFEJRTTYYDIOB9AW8F5XjJsN69e9sAGI1GscocLy3mbBs5ciTNmjXL5nCuRBYfH0+lpaU2+yp32AvK8dKacsvZVZ6j8ucczlu8eHHlrorPORh17Nixim17n3AIMD8/X7LaGv/yfcKECfYOd8s+Du5xgM+6ZWRkEC/n6sja+jje5mVtN2zYILWLPv30Uxo6dKjkPiWd9oJyHFhs1KgR8bV7o2VmZlJ0tO1/6C9btozuvfdeb1yS5DkRlJNkke1EUE6WBjvcIMBB3ylTpkjOzNU9+/btS6tXr5bcj073CzgblON/13AYXmmwvqp3gqDcv4KOAnLmkcEUlAurGS9WiTPfu/l11zvTqTj73zd/hNerTa1GPE4aLf+YoLxl7NxLx7/7Sdxo88xQql7HsuLy+a1/0pmV68zD8QoBCEAAAhCAAAQgAAEIQAACELAQ0IZFEC/VGsLBOfGjgVCtPMJijNwG/2ys5PJ5KuGqcxevVJ7LFMJzpUa5Q9APAQhAAAIQgAAEHAogKOeQCAMg4HsC3gjK8bKaHAqKiLD9D5g//viDunbtWiWo1q1bEy9XKdU6depEO3bskNpV0WcvKFevXj1KSUmpGKvkE74frgYj1XhpUq6Ap7QdPnyYeHlS63bu3Dnq0KEDpaWlWe9y2zaHyk6cOCE5P/9Cn6vcOdP0ej1xoE0qNMZWbOZssxeUe+utt+j11193duoqHcd/JjgkKtUWLFhAjz/+uNQur/QhKKeOHUE5dV4Y7bzAjTfeSBs3biT+HirVvBWolrqWYO1zNijHXhyS478PXn75ZTE0705DBOWIlAbkzM8hmIJyHIBrPeoJ861XvO546wMy5pUva8PhuFYjn6Dwuv8G4Yx5BbTngzkVY1o8OYgiGydVHM+fpP21g04tW2XRhw0IQAACEIAABCAAAQhAAAIQgIA9AV14bPmSrRygi+Pqc0J4TqhGp6SVmUqpJDNVqDxXHpzj15LLqUQmk5LDMQYCEIAABCAAAQgQgnL4IoCAHwp4IyjXsWNH2r59u6QWLzNZuTIaLyPHzfwq93nl/VqtlnhJUH61bvfddx/98MMP1t0W23JBOQ4yhYaGCv+NpO4/kuwFyrp3705btmyxOL+9jR9//JF4SVOpxiFDXrJUaYU6qTnU9LElL2cr1aq6ROqqVavo9ttvt5m6pKSEatSo4fTSc/aCcrGxsWKA0+akHujgr1W+N6mvWa5gyJUMfaUhKKfuSSAop84Lo50TaNy4MfHfATVr1pScgKu4cjU5tX9/SU6GTqcFqhKUM5/0r7/+ojvvvJPS09PNXS5/DeagnNqAnMvxPTTh3y+97fSZIpLqU8unh9gcv+ON98hYUL6sc61u11GDO3pZjOFKclxRztyaDh5AMa2bmzfF1/Tte+jEkuUWfdiAAAQgAAEIQAACEIAABCAAAQioFdBHJlhUnTPE1ietIVTRNFxhrvjS2fLw3MUzwpKtp8mYdYFIqEiHBgEIQAACEIAABKwFEJSzFsE2BPxAwBtBuaeeeormzZvnFZ0xY8bQ9OnT7Z5bLih3+vRpSkpKsnus1M6wsDAqKCiQ2kW9evWideuULzE1fvx44l+0yzVe6pTnW7JkCa1Zs4bOnj0rN7TK/ePGjaN3331Xch61AUDrST744AN67rnnrLvFba6od/ToUcl9jjrlgnLcLxcwcTSnq/ZzNcCEhASb6VasWEH9+/e36fdWB4Jy6uQRlFPnhdHqBXhJcQ7J8VLeUu3UqVPUuXNnry0rLXVNwdrHS6jzR+UWHh5OHHRs0qSJ+HHttdfSbbfdVnmIzef8dyCHyY8fP26zzxUdwRqUC42NphbDHqHQqEhXMPr0HFUJyoXXr0OtR9pWut09ZSYVZWZRaHQktX3+adJW+lrPOnKcDn+20MJEKiiX9vdOOvXDzxbjsAEBCEAAAhCAAAQgAAEIQAACEKiygFCQwRBVq2K5VgMv2xpblzQ6g6KpTcYiKkkXQnPiB1efE8JzOe57E6Oii8IgCEAAAhCAAAR8QgBBOZ94DLgICKgT8EZQ7pVXXqFJkyapu1AXjeZgFy9bZq/JBeW48hsHwNQ2rhLGATappjYox8vVHjhwgHgJWCWNl0b9+++/xQ9ecpaXLs3KylJyqMMx77zzjqxlYmJilZaBHTZsGM2dO1fyGq655hr6559/JPc56pQLyrHRdddd5+hwt+7fv38/tWrVyuYcrliO2GbSKnQgKKcOD0E5dV4YrU6AQ1br16+X/f7Fy5zz8t8HDx5UNzFGe1WgW7duNG3aNNnnyhfHf7+3aNFCrEbq6osN1qAcO6KinOOvpmqJCdT2uWE2A/fP/JTyUs5RsyH/R9Etm1XsNxWX0N7/zhNCdJcr+viTZo89SNHNG1v0nd/6N51ZucaiDxsQgAAEIAABCEAAAhCAAAQgAAG3CAi/tzHE1K0Iz4UI4TlDdG3SaHWKTldanC+E5/5dspVDdKV5mYqOxSAIQAACEIAABAJHAEG5wHmWuJMgEvBGUO69996jsWPHekV56tSpxJXQ7DW5oNx3331HDzzwgL1DJfe5MijHJ+ClV3kJVmcbh8y+//578cPZymx87lmzZtHIkSMlL0Ov18uGAyUPsOqU+yU9D+vZs6cYDLE6RNGmXFBu8eLFNHDgQEVzuGvQpk2bqEePHjbTHzp0iFq2bGnT760OBOXUySMop84Lo5ULGAwGWr58OfXp00fyoMLCQrFq6datWyX3o9P3BYYOHUoff/yx7IXy38GzZ8+W3e/sDrm/g3m5V6nKp86ex5ePUxuYu/D7Njq94ldfviWXXVtoTDS1HzfKZr5Dn35DutBQajrofot9Z35eS+e3/GXRxxstnhpMkVc1tOhP3bCVktdssujDBgQgAAEIQAACEIAABCAAAQhAwFMCGp2eDHH1KSROqDgX30AM0emjEkkjVKRT0koLc6j4YnnFOa46x+E5k9CHBgEIQAACEIBA4AogKBe4zxZ3FsAC3gjKffbZZ/TYY495RbUqQbmvv/6aBg8erPq67QXlevfuTWvXrlU956uvvkoTJ04kDqRVpXFojp/Fvn37VE/z1Vdf0aBBg2yO43BGtWrVbPrVdHDw45dffpE85N5776Vly5ZJ7nPUKReUU/J14Wjuqu7n5XI5nGDdeElWrtDnKw1BOXVPAkE5dV4YrUyA/17hv5MefPBByQO4iumAAQOc/l4pOSk6vSLw4Ycf0rPPPit57nPnzonLtsot7y55kIJOBOX+RVIamAumoJw+vDp1HP/8v0hXPuMlU+v27EGGyBoV+/KSU+nA7AVUZiqr6DN/0m7s0xSWEG/eFF+Tf91IqRt/t+jDBgQgAAEIQAACEIAABCAAAQhAwJsCGn3oldBceXCOA3T6iATFl2TMuySE54RlWzPKg3O8hKtJqEaHBgEIQAACEIBAYAggKBcYzxF3EWQC3gjKyQWCmH7nzp0uWxrU+lGWlZURL/v6559/Wu+y2JarKOdLQTm+YF4qlMNqTZs2tbh+tRv8C/ZnnnmG+L7VNK5Kx6E165abm0u8RGxVGleNkwsQcliRn4UzTS4oN2LECJozZ44zU7rsGK4KNHz4cJv5jEYjceUoX2kIyql7EgjKqfPCaGUCM2fOpFGjbCs6mY9+8sknVX9PNx+LV98SCAkJod9//506d+4seWGjR48WK7xK7nSyE0E5WzhHgblgCspptBrq/PbLwlI0Wgso/nd25XfYl5lMxMux5p+7YDHOvNFp4gukCws1b4qvJ5auoPR/dlv0YQMCEIAABCAAAQhAAAIQgAAEIOBrAtqQ6mK1OQNXnUsQqs/FCeG58BjFl2nMThOCc0J4Tqw+xyG6s1RmLFJ8PAZCAAIQgAAEIOA7AgjK+c6zwJVAQLGAN4Jyn3/+OQ0ZMkTyGvl6zp8/L7nPU53+EpRjD51OR1yVjiu73X333cQhJmfbSy+9RFOmTFF8OIfVHn74YcnxYWFhVFTk/H/Y8RK3CxculJybf4HPIT1nmlxQjv2++eYbZ6Z02TFvvPEGvf7665LzxcbGUmZmpuQ+T3ciKKdOHEE5dV4Y7VhgwoQJYkVRuZEcCJ88ebLcbvT7oQD/Pf/rr9LLen7xxRf06KOPuvSuEJST55QLzAVTUI512o0dLlSDi5OHEvZwZTiuECfVeInWTm+8YLPr4NwvKOfUWZt+dEAAAhCAAAQgAAEIQAACEIAABHxdQBsWUR6ai+fgnPCR0EB4g5iyggL85jNj1nlhqdbyqnMcoCvJTKayUqOv3zauDwIQgAAEIBD0AgjKBf2XAAD8UcAbQbkPPviAnnvuOUmuhg0b0pkzZyT3earTn4JylU1q1KhB3bt3FyvNXXPNNcQf8fGWS1pVHm/9eX5+PrVo0YLOnlX2C8qPPvqIuBKbVEtKSqLTp09L7VLUx18f/HUi1Xr16kXr1q2T2uWwz5eDcg899JBsWO+WW26hjRulf9ns8KZdPABBOXWgCMqp88Jo+wJc/XP69Omyg/j75tixY2X3Y4d/CvDf5fz3l1TjanM33nij1C6n+xCUc0xnHZgLtqBcsyH/R9Etm8lCFaZfon0fzieTUBVXqkVc1ZBaPjXYZtfOt/9LJbl5Nv3ogAAEIAABCEAAAhCAAAQgAAEI+KOATqgyF8LBOfGjAXEFOp1QjU5JKzOVCmG51H/Dc8LSrbxNQgV3NAhAAAIQgAAEfEcAQTnfeRa4EggoFvBGUO61116jt956S/IaOah1+PBhyX2e6vTXoJy1Dy9/1b59e7Hi3G233UY9evQQK9BZj6u8vXjxYho4cGDlLtnPuWIRV6GTal26dHG4xK3UceY+rmz34osvmjctXq+99lratm2bRZ/SDV8OytWtW5eSk5Mlb8WXKkQhKCf5iGQ7EZSTpcEOlQKPPPIIcUXWyksbVp5iwYIF9MQTTxC/AxUt8AT4TQT169e3ubG0tDRKTEy06a9KB4JyyvXMgTn+c5m8ZpPyA/18ZP2+t1Lt7l1k7+LQ/K8o+4T8GyZqd7+e6vftaXG8saCAdrzxvkUfNiAAAQhAAAIQgAAEIAABCEAAAoEmoI9MsAzPxdYnrSFU0W1yhbniS2f/Dc8JFeiMWRdI+IGgouMxCAIQgAAEIAAB1wsgKOd6U8wIAbcLeCMoN3z4cJo9e7bkvfXr149Wrlwpuc9TnYESlLP24hAiL+85YMAA2aCFSXg3Unh4OBUWFlofbrPNITm55f3uuece+vHHH22OUdrx5Zdf0uDBtpVG+PiqhCl9OSjH93bs2DFq3Lgxf2rRfvrpJ3FpXYtOL20gKKcOHkE5dV4YLS3AS2svXbpUNuzMy1FzyLm0tFR6AvT6vcDWrVupa9eukvcRERFBubm5kvuc6URQzhm14Domqlljav74g5I3fXHbLjr5vf1/y7cQqslFClXlKrdLew/SsW++r9yFzyEAAQhAAAIQgAAEIAABCEAAAoEvILz5zhBVSwzPccU5sfpcbF3S6AyK7t1kLKKS9DNCeI4/ypduNeZIr0ygaEIMggAEIAABCEBAlQCCcqq4MBgCviHgjaAc/6KXf+Er1Th8xdXEvNkCNShnNuUQ25IlS2QDF1yFbs+ePebhsq8cyli0aJHkfl6Sdc6cOZL7lHSuXbuWeva0rDTCx3EIhAMBBULVEWearwflPv30U3r88cdtbu38+fPEf1Z9oSEop+4pICinzgujbQV46eVVq1ZRaKj0O0vXrFlDHDIvLi62PRg9ASPAFUe58qhUS0hIoPT0dKldTvUhKOcUW1AdpNXrqeOEscI73i1/aG8qLqFd70wno503XIRERVL7l0bbvGnjxJIVlL59d1A54mYhAAEIQAACEIAABCAAAQhAAAKSAlotGWLqUkicEJxLKF+61RBdmzRaneRw687S4vwr4TkOzpWH50rzMq2HYRsCEIAABCAAARcIICjnAkRMAQFPC9SqVYvOnTsnedpOnTrRjh07JPdVpTMkJISysrIoLCzMZpply5bRvffea9PvyY5AD8qx5ejRo2nGjBmSrBzs4SCdo9a0aVM6cuSI5DA+nudxpnEQi78mIyMjbQ7fv38/tWnTxqZfaYevB+V4acUvvvhC8nYaNmxIvPSetxuCcuqeAIJy9r04OD127Fj7g4S9HBJ65plnHI4LtAHXXXcdrVu3jmrUqCF5a7///ru4vHZ+fr7kfk91cnXR5s2bOzzd3LlziYN9aOoEYmNjKSMjQ/Yg/vdUUVGR7H61O3wpKMfLxj/77LMOb+HUqVP0/PPPOxyHAa4TaDrk/yimZTOLCU1GI+2e+hGVZOdY9FfeSLq3L9W8tmPlLioTKhpzwK4kN8+iHxsQgAAEIAABCEAAAhCAAAQgAAEIlAtodHoyxNYTKs4lCR/llef0UYk2b0ST8yotzKHii0JoLkP44FehAp1J6HNrE6rlxXQZSJf/+p7KSkvceipMDgEIQAACEPCWAIJy3pLHeSFQBQGuzpWdnS05Ay/1xks+uqNt2rSJ+Jef1o0r4tSpU8fuL4Stj3H1djAE5WJiYkRjjfAfKtZNaVU/PpYDj/w1ZN1KSkqoXr16lJaWZr3L4fZjjz1Gn332meS4r776ijhM5mzz9aAch+H4l/1SjSv4LV68WGqXR/sQlFPHjaCcfS85H+ujDh48SK1atbLuDujtdu3aEf9dyd+vpRoH2bnaHH8f9nb7448/qEuXLg4vY9iwYTR//nyH4zDAUuCmm26ijRs3WnZe2XJHxVFfCsoNGjSI+O9+R2337t3UoUMHR8Ow34UCEcLSqS2FJVStW9aR43Tki8XCD8Btl4KOanoVNROWbLX+92f6jr10YrF7/pvD+vqwDQEIQAACEIAABCAAAQhAAAIQCBQBjT70SmjuypKtQgU6fWSC4tsz5l2yWLKVl3A1CdXoXNUMMXWo1t2vUvGlZMpYP5+MufJvBHXVOTEPBCAAAQhAwNMCCMp5Whzng4ALBPgXVUah+oNWKOVs3UaNGkUfffSRdbdLtjmMxRVopNp//vMfev/996V2eaQvGIJyemHJLF6+lF+tG1dk+e9//2vdLbm9fv16MaghtXPcuHE0depUqV12+/7880/iKkpSjSvhzZo1S2qXoj5fD8rxTXBQjgNz1m369Ok0ZswY626PbyMop45cLgjGIdLExER1k7l49L59+6h169Y2s164cIG42qgnmpyP9bmDLSjHFTu3bNki+zXCHt27d3fpcpvW5mq2EZRTo6VuLP877ZdffqHbbrtN8sAFCxZILtktOVhhJ4JyCqEwjFo+PYQikurbSHBY7tSyX6go87K4T6PVUHzH9tTw7j7Ey7ZWbmVlZbT3/blUmI4flld2wecQgAAEIAABCEAAAhCAAAQgAAFnBLQh1clwpeJcReW5cOk34krNb8y+WF51TgjNlVegO0tlRudWMghv2oVibxwknoaXg720aQEVphyQOi36IAABCEAAAn4rgKCc3z46XHiwC6Snp1NcXJwNw5dffklDhgyx6XdFBy8jxstIhoeH20yXm5tLV199NR07dsxmnyc6giEox1VXdu7cKcn50EMP0cKFCyX3WXcOHTqUPv74Y+tucZufX7NmzYh/Aaq0cQUlrgoj1TjQyVXqOMTjbPOHoNy0adOIw6LWLSUlhRo0aEAmYXkybzYE5dTpywXBEJQrd5TzsVYOpqAc/znnkBy/SrUTJ05Qt27dKDU1VWq3V/oQlCtnj46OplKhilZOjuuWrXjhhRfshs451Pb999+79LkjKOdSzoCeLLxubWo5fIhN+I1vmivK5aWcF/bpSF8jnEIiIyQtzm3+k86uWie5D50QgAAEIAABCEAAAhCAAAQgAAEIVF1AGxZBIQkNy6vPxQlLtyY0IJ3Qp6Tx73eMWecrVZ47TSVChbiyUqPDw2O6PEA1WnSrGMdzZe9cSdm7V1f04RMIQAACEICAvwsgKOfvTxDXH7QCGzZsoJtvvtnm/vmXvbzU3ZEjR2z2uaJjxowZxBXCpNquXbvonnvukV2GUuoYqT4OVo0dO5YefPBBxRWSfDUo17hxY3r55ZfFimrsU5U2Z84cevrppyWn4OXzuKqbksbLrvKybxyekmpqKsDpdDqxak6vXr2kpqLly5fTXXfdJblPaac/BOWaNGki/pmzXpaM77Fnz57EVfy82RCUU6cvFwRDUK7cUc7HWjlYgnJcZZBDclxRTqpxYJZDcidPnpTa7bU+BOXK6Z955hl66623xOVluQpocnJylZ4JP2v+nm8wGCTn4WXO4+PjKTs7W3K/s50IyjkrF5zHxXdsR1f9X3+nbj772Ek6/Nm3VGZS/qYKp06EgyAAAQhAAAIQgAAEIAABCEAAAhCwENAJVebKK84JwTmhAh1XodMJ1eiUtDJTKZVcPidUnDtVHqDLEMJzmcKbeq3e5J/Y70Vh7oY2Uxac3UsZv31OZSWFNvvQAQEIQAACEPA3AQTl/O2J4XohcEVAroIV7+Zf2HNVOXf8Up6r5ezfv59q1Kgh+Szy8vLolVdeEYNhaqtoXX/99fTUU0/RoEGDxF8wFxUVUVhYmOR5rDt9NSjXsWNH2r59u3i5a9asoQ8++IDWrVsnVq+xvgd727yk7syZMyWHcJCMl1xU4/3ZZ5/RY489Jjkfhy0HDBhAy5Ytk9xv7uQlYDm8xxXq5Fr//v1pxYoVcrsV9ftDUI5vhJ+vVGDwiy++oEcffVTRvbprEIJy6mTlgmAIypU7yvlYKwdDUC4mJoY2bdpEXFlTqvHXTI8ePejQoUNSu73ah6BcOT8H5Tggx41DbD/++CMtXryYVq1aRfn5+eWDFPw/ByXfeOMNGjhwIGm1Wtkj1q5dS71795bd7+wOBOWclQve42pe15Ea3NmbtAbLZVXtiWTuO0Qnlq6g0kLnlm+xNzf2QQACEIAABCAAAQhAAAIQgAAEIKBeQB+RUF55Lq6B+GqIrS/8t36ooonKSkuoWKg0V8xLtqaXV53joJxGK/2zAmN2GqWvny8G7hSdAIMgAAEIQAACPiqAoJyPPhhcFgQcCXTt2pW2bt0qO4xDZvPnz6cdO3aIFd7Onj1L3CfV1FZP4Upv3377rdRUFX38C/hXX32V9uzZQ5cuXaror/wJh+A4SNanTx/ipUO5+lrlFmhBOfO98TKkS5cuFT/+/vtvu7+Iv/baa+nFF18UK/XJ/eJ99uzZNHLkSPP0il7r1KlDBw4coKioKNnxHIKbPHky8deOdbvhhhvoww8/pGuuucZ6V8X2r7/+Kj7big4nP/GXoNy9994ruZQeL+fHFacKCgqcFKj6YQjKqTOUC4K5MyjH4eYRI0Y4vNC2bdtStWrVbMbxMsf8/d5Re++992jJkiWOhtndL+djfVAwBOXefvtt8e8663uvvM1/D7qq8d+nUtVknZkfQblytcpBucqOHJLbvHmzuKQ8L51r/uA3IRQXF1PDhg2pUaNG4gdXdX344YeJA+T2WmZmJnXu3Fmcy944Z/YhKOeMGo6pnliT6vW9laKaXiX8EJx/NCDdCtMv0bnf/kcXt+2UHoBeCEAAAhCAAAQgAAEIQAACEIAABHxDQKMhQ1QtsdocV4YTK9DF1iONTnr1A7UXbTIWUebWbyj/ZHmBCLXHYzwEIAABCEDAFwQQlPOFp4BrgICTAryUZ/v27Z08uvwwDlfILQ9mb+J58+aJ1d/sjTHv42AYByZ4OVg+X1xcnPiL5Q4dOlBISIh5mM1roAblKt8oe+zdu1cMFHIgLDc3V/SpX7++GBzkUIy9dvnyZWrevDlxgEdt4+p9/BzttbKyMuIwxeHDh8VrS0hIoOuuu46uuuoqe4cRVxZs06ZNlZfh5ZP4S1COAxKnT58mDiFaNw6CLly40LrbY9sIyqmjlguCuTMo99JLL4nBVHVXqn60mqWV5WaX87EeHwxBuXfffZfGjRtnfetu23bl1yCCcuWPSS4o5+qHyFVfb7/9drH6qKvn5vkQlHOHavDMaYioQTEtm1JIdBSFREUQB+P04dWpOCuHcs8kU+7p5ODBwJ1CAAIQgAAEIAABCEAAAhCAAAQCTUBY/cAQU4dC4q4E5xKSyBBdW3jTnM7pO83Zv4EubxNWJSozOT0HDoQABCAAAQh4SwBBOW/J47wQcIEAV/X67bffHFYwsXcqZ4NyoaGh9Omnn4oVVOzNX5V9wRCUq4oPh9gGDx5M33zzjVPTaIR3Fn399ddiNT+nJpA5iJdu5eVzFy1aJDNCXbe/BOX4rnjZvddff93mBnkJvzvuuMOm31MdCMqpk5YLgrkypGR9Rf4UlOOlhBcsWGB9CzbbCMrZkFS5w5Vfg0rD9sOGDRMr1Fb54n10Ak8F5fjP+JQpU9ym4EtBOSVBfIbYvXs38Zsm0CAAAQhAAAIQgAAEIAABCEAAAhCAAAQ8K6DR6ckgVJorrzpXHqDTR9cijfA/pa3o/FFK3/gpmQpzlB6CcRCAAAQgAAGfEEBQziceAy4CAs4LcGDho48+Ig7CONOcDcqZz8WhIA4HuaMhKCevyiE5/kX0J598Ij9IwR5ezvXjjz+mxx9/XMFox0NKSkrE4B0vLeuq5k9BuXr16olV9HQ6y3di8Z+zBg0a0Llz51zFomoeBOVUcRGCcva9+HvG0KFD7Q8S9vL3pyeffNLhOH8e4K8V5SIjI8Vl0a2/V1k/C/67hivXcuXTQG3uDsrx9/933nmHJkyY4FZCXwrKffHFF/TII484vF9nlo53OCkGQAACEIAABCAAAQhAAAIQgAAEIAABCDglkHj3axQSU1vVsaX5l4Ww3CdUnHZS1XEYDAEIQAACEPCmAIJy3tTHuSHgIoHGjRvT3LlzqWfPnqpnrGpQjk/Yq1cv8ZfAnTt3Vn1+qQM4bLV+/Xr6/vvvFQfBOJDxxBNP2EzHFdO46praxgEyrowm1Xr37k1r166V2mXTx8uibt26leLj4232OduxYsUKMZy4fft2Z6ewOI4ry/3nP/+h8ePHU0REhMU+NRtcPWrEiBG0adMmNYc5HOtPQTm+Ga60KBU8nDFjBj377LMO79cdAxCUU6eKoJx9r2PHjolLQ9sbVVxcTE2bNqUzZ87YG+b3+/w1KHfnnXcS/13iqHFl0AcffNDRML/e37dvX+Lvz/xvKVe3ffv20WOPPUb//POPq6e2mc+XgnL8556XkLfXCgsLRfPU1FR7w7APAhCAAAQgAAEIQAACEIAABCAAAQhAwAMCGr2B6g76gDQaraqzlZUWU2HqYbq05SsyFeWpOhaDIQABCEAAAt4SQFDOW/I4LwTcIFC3bl3iEBd/8C98Y2NjxY+oqCji4JdUS0lJIa6C5Yp2991303PPPUddunQhg8Ggasq8vDxavXo1LVu2jFauXElZWVmqjp80aRK98sorNse8//77YgjMZoeDDg6PXbhwgRISEmxGcnWdPXv22PTLdfBcvLQYBxk5VHjjjTdStWrV5IbL9i9fvlwMyO3YsUN2TFV2JCYm0ptvvkkPPPAAcbUhpe3kyZP0wQcfiGFNDl66unHQoHXr1jbTsuW6dets+r3dUbt2bTp69CiFh4dbXIo3QwEIylk8Cocb3gjKjR49WgzrOLy4Kg7ge1uyZInTs3D4RUn4bdasWcT3FOiN/87j73+ealzZrV27dlU+Hf/d+Pzzz9udh8Pi/L338OHDdscFys6GDRvSrbfeKn7ccsstVKtWLadujd343zFcLY1D9VyVzxPNV4Jy/O9PDtM6avznZuzYsY6GYT8EIAABCEAAAhCAAAQgAAEIQAACEICABwRCa15FNe9w/LMa/llXScYZMRxXmHqQii+coDKT638v5IFbxikgAAEIQCCIBRCUC+KHj1uHgLsEuCpZjx496OabbxZDeFxNjQNnHK5ISkqis2fPih+8zZ+fPn2a/vrrLyooKHDXJfnUvKGhodS1a1dq2bIlcaiqTp06FR9swn2nTp2y+Dhw4AAdP37cI/fBIcdu3bpRnz596KqrhP84qllT/OBffHOoMi0tjbjK286dO+nnn38mriSHZikgtyQxL5M8atQoy8Ee2LIXlOPQZ25uruRVcFXHqoSAOr85TnJecyd/b3jgwQfMmxavHFQtM0kHTHbu2knHj0n/efjn9SkW8zizIReU42qX27Ztk52Sg0f8vSyQGy+nyMsq2mv8vZzDMt5aatjetWFfuQAHrq+++mq7HPyceXn3YG0cEuRQIofIOTTHr+aPzMxM8XNeIv7y5cuUkZFBu3btEv/8c/W47Oxst7BNmTJFDNtLTR4XF0dcxda6paenS4b+rce5apuXZeblme01fnNEo0aNxH9L2BuHfRCAAAQgAAEIQAACEIAABCAAAQhAAAKeEajR6iaKuW6A5MmMOReFYNwhMRxXdO6wUDkuX3IcOiEAAQhAAAL+IoCgnL88KVwnBCAAAQj4jQAH044cOUJc5bFy41AFL0fJAVFPNntBOXvXUdVgX1WCcvaua8vmLbR7927JIe4MykmesFLn7bffLlbGrNQVcJ9+/vnnNGTIELv35WwlT7uTYqfLBLjaLIed5SrN8ok4FMqhK64YiuY7AlxZtl+/fqouyNNBuW+//dbhcr2TJ0+WrAKs6sYwGAIQgAAEIAABCEAAAhCAAAQgAAEIQMBlArHdh1B442vF+UoLc6no/BEqTDlERUJAzpib4bLzYCIIQAACEICALwggKOcLTwHXAAEIQAACASfAYSIOFVm3uXPn0vDhw6273bqNoJw6XrmKco5mCYagHFcAbdCggSwFVyfkSlEczkHzTYF77rmHfvjhB7sXN2/ePHr66aftjsFOzwv4Q1AuNTVVrIwrp5OVlSV+j+CqfGgQgAAEIAABCEAAAhCAAAQgAAEIQAACviEQ0+UBMRDHleNKLiUTCUusokEAAhCAAAQCVQBBuUB9srgvCEAAAhDwqoBGoyFegq9jx44W11FcXEzNmjUTlxy22OHGDW8F5Tq98SKxg1yzt/Sq3DHcL1dRrkz4j/ftE6baO1TRPgTlpJl4KWZHS0BPmjSJXnvtNekJ0OsTAjNmzKDRo0fLXgtXvmzSpAklJws/EEPzKQFfD8q1aNHC4XLsEyZMoDfffNOnXHExEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIBI8AgnLB86xxpxCAAAQg4GGBm266iTZu3GhzVg6qPPvsszb97uqoVq0a5eXl2Q2tSZ27qkuvdpz4gt3lHePi4xwu0Sd1XXJBOZPJRDsmTpM6RFXf/fffT0uWLFF1DA8O9IpyTzzxBH3yySeyLpcvXxYrRfErmu8K7N27l9q0aSN7gdOnT6cxY8bI7scO7wksWLCAHn30UVUX4MmlV7la6uzZs2WvLyMjQ/wekZOTIzsGOyAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEICAOwUQlHOnLuaGAAQgAIGgF+CKciEhIRYOHBY4evSoRZ+7N7hCFFdwU9POnz9Pp06dUnOIxdiOr48lrV5v0We9ERcXR3qD/THWx+Rk51B+fr51N5mMRtrx5vs2/Wo7uAoePzeDwaDq0IMHDxIvKxio7euvv6aHH35Y9vbGjx9Pb7/9tux+7PC+QEJCAl24cEE2NMt/rrhyII9B8z2B8PBwatu2raoLKykpoe3bt6s6xtnBixcvpgEDBsgePm7cOJo6tepVP2VPgB0QgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAwIEAgnIOgLAbAhCAAAQgAAHnBK5+dQzpQkOdO9iJo0qFJSN3TvrQiSNxiBKBlJQUqlOnjuRQrlrFAStUipLk8ZlODjFxmEmucYiJw0xoEFArwAFjDlhyGFOq8T7+HiEVcpYajz4IQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQg4A4BBOXcoYo5IQABCEAAAhCgdi+OopAa4R6TKM7Noz1TZ3nsfMF0oubNm9OhQ4dkb/nFF1+kadOqvuyt7AmwwyUCc+bMoaefflpyruzsbDHIxBUv0SCgVoAr3e3Zs0f2MF7Ol5f1RYMABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAAC3hRAUM6b+jg3BCAAAQhAIIAFWo8eStUS4jx2hwUXM2j/zE88dr5gOhGHqzhkJdV4id7GjRujUpQUjo/1cdiRQ49S7c0336QJEyZI7UIfBBwKPPPMM7JBuOTkZOLlv4uEqp9oEIAABCAAAQhAAAIQgAAEIAABCEAAAsEhoA2LoGoN2lFIfAMyRNUiXXgUaQzVSKPTU1mpkcpKCqg0L4tKss5TcfoZKjizh0yFOcGB42d3aQivTtGtm1N4vdrC73ziyRAZQfqwMNLotVRmNJGxsJBKsnOo4GI65SWfo8v7D1NJXr6f3SUuFwIQCCYBBOWC6WnjXiEAAQhAAAIeFGj22IMU2aiBx86YffIMHVmw0GPnC6YTffLJJ/TEE09I3jIHZGbOnCm5D52+IxAVFUWXLl0irVZrc1GZmZnUqFEjysrKstmHDggoEfjyyy9p8ODBkkM5aDtv3jzJfeiEAAQgAAEIQAACEIAABCAAAQhAAAIQCCwBXXgsRbbvQ9UaXU0arU7xzZWZSqng5E7K3r1aCNBdUnwcBrpPICQ6kure0o1i2rYkrU75szSVllLm3oOUsmELFV/Odt8FYmZFAiHhekpsHkVRdapTjfgwCo0wkCFMJ4RWNUJotYxKCkupKKeEctMLKSs1ny4czqLiPKOiuTEIAv4qgKCcvz45XDcEIAABCEDAxwWS7upD8Z3ae+wqL/6zm04vX+2x8wXTiWrWrEldunShsrKyig+TySR+vmHDBlSK8pMvBn6G/Cz5OZqfH3+emppKu3bt8pO7wGX6okCtWrXouuuuq/j+UPlrbN26dVRSUuKLl41rggAEIAABCEAAAhCAAAQgAAEIQAACEHChQHiLbhR19Z1CpTGD07OWGUsoa+dKyju0xek5cGDVBWpe35nq9epBWoPe6clMJUZKXvsbpf35j9Nz4EDnBapFhVDj7olUq2W0EHTkWJCyZhLCc+cPXqbjmy9QQVaxsoMwCgJ+JoCgnJ89MFwuBCAAAQhAwF8E4ju1o6S7bvfY5Z766RdK377HY+fDiSAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACQS+g0VJMl4FUvfE1LqPIP76NMv/3HVGZyWVzYiLHAhphRZKGdwtFEDq0dTxY4Yj0XXvp9I+rhUeJZ6mQrMrDGnSOp2Y31yatXnlAzvqkJmMZHdl4js78k269C9sQ8HsBBOX8/hHiBiAAAQhAAAK+KWCoEU5txw5XVZLb2TvhUt57359DJbl5zk6B4yAAAQhAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEFApEHPDgy4NyZlPL4bl/lho3sSrBwSS7u3r0pCc+ZI5LHfqh1XmTby6SUCj1VDrvvWoTtsYl50hdW8m7V+VLAQdy1w2JyaCgLcFEJTz9hPA+SEAAQhAAAIBLNDo/jsprl1rt99hxp79dHLpSrefByeAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCECgXICXW42+5h63cVzetgzLsLpN13JiXm61Qd9bLTtduHVm1Xosw+pCT6mp2txZ36UhOfM5OCy3b+VZ8yZeIeD3AgjK+f0jxA1AAAIQgAAEfFcgNCaKWo8eKpR31rvtIk1GI+2f+QkVZWa57RyYGAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAgX8FdOGxlNh/HGn0hn87XfxZmbGELiyfQqV5l1w8M6arLBASHUltRj9JWoMbf5dTYqR9Mz+m4svZlU+Nz10kwMuttuhVx0Wz2U5zaG0qlmG1ZUGPnwogKOenDw6XDQEIQAACEPAXgcQbrqH6fW5x2+WeXb2BLvyxzW3zY2IIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAhCAAAQsBWJueEhYcrWzZacbtvKP/0OZf3zrhpkxpVmg0b13UFyHNuZNt71m7NpHJ3/42W3zB+vE1aJCqOtTzYWiFRz/cU8zGcvo9/mHqSCr2D0nwKwQ8KAAgnIexMapIAABCEAAAsEq4K4lWLHkarB+ReG+IQABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAwFsC2tAaVOv+CaTR6tx+CWWmUjq/9A0yFeW6/VzBeAJ9eHVq958RpNW5/1maSktpz3uzyZiXH4zUbrvnNv2EJVfbxLhtfvPEqfuEJVhXYAlWswde/VcAQTn/fXa4cghAAAIQgIDfCGi0Wkq6ty/FtWvtsmvmkNypH1ZRmcnksjkxEQQgAAEIQAACEIAABCAAAQhAAAIQgAAEIAABCEAAAvYFqje5jmK6DLQ/yIV7M//3HeUf+8uFM2Iqs0Bcx3bU6O7bzZtufz354y+UsWOP288TLCcIqa6j7qNaCUFH91WTM1uaSsto86wDVJxfau7CKwT8UgBBOb98bLhoCEAAAhCAgH8K8DKsdXt2F8o/652+AZPRSCnrNmO5VacFcSAEIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAwHmB6OsHUHjTLs5PoPLIvKP/o8t/LlF5FIYrEWjYvw8ldG6vZKhLxlz8ZzedXr7aJXNhEqK67WKp9R31PEax/+dkStlzyWPnw4kg4A4BBOXcoYo5IQABCEAAAhCQFQiNiaI6t3ajmNYtVJXy5pLcmfsPUer6LVSUmSU7P3ZAAAIQgAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCDgPoH4XiMotFYT953Aauai80cpfe0cq15sukKg2WMPUmSjBq6YStEc2SdP05EFixSNxSDHAq1ur0v1OsQ5HuiiEcm7MujALykumg3TQMA7AgjKeccdZ4UABCAAAQgEtUDjxo3pjvvupdKasVQaWYNM1cOoLDSEyvQ6KhOWadUIy6lqjKWkKSombX4h6bJzSZd2iVYu/Z5OnDgR1Ha4eQhAwH0Cd955JzVpou4HfMXFxTR79mz3XRRmhgAEIAABCEAAAhCAAAQgAAEIQAACEPAbgU6dOlG7du0srjc3N5eWLPFsNbTevXtTq1atLK7D0UZZWRlNnz7d0TBxf2L/l0gfVVPRWFcMMmal0YXl77piKsxhJdB69FCqluC5oFXBxQzaP/MTq6vAprMCnR++imIb1HD2cNXHXTqdS/986/zv6caMGaP6nAcOHKA1a9aoPk7tAfy7y+7du1scxt8XFy1aRIWFhRb92PBvAQTl/Pv54eohAAEIQAACfilw991307Jly1Rf+1133UXLly9XfRwOgAAEIKBEYOnSpXTfffcpGVoxJjs7m6Kioiq28QkEIAABCEAAAhCAAAQgAAEIQAACEIBAcApUq1aNDh8+TPXr17cAOH/+PNWrV49KhVVTPNU+//xzGjJkiKrTcSBEK7yRXUmrff9E0laLVDLUJWNMBdl0bulEl8yFSSwF2r0wkkIiPBe0Ks7JpT3TPrK8CGw5LXDDk82pRnyo08erPTA3vYj++Piw2sMqxpu4UIaGY0rKG38/e+yxx5Qf4OTIQYMG0VdffWVz9Msvv0zvvougrg2MH3cgKOfHDw+XDgEIQAACEPBXAQTl/PXJ4bohENgCCMoF9vPF3UEAAhCAAAQgAAEIQAACEIAABCAAAXcKvPbaa/TWW2/ZnIIrKCmt1GZzsJMd7g7K1XlgMmkMngvnlJUUUuqiV5zUwGH2BK5+dQzpQj33LEuLimjnpA/tXRL2qRDoMboVhdbQqziiakOLco3028wDTk/iy0E5g8Eghp0bNWpkcX85OTniSjRpaWkW/djwXwEE5fz32eHKIQABCEAAAn4rgKCc3z46XDgEAloAQbmAfry4OQhAAAIQgAAEIAABCEAAAj4vMGDAAOKKVNZty5YtdPLkSetubAeIQNu2bal69eo2d3P69GniSmRo/iFQq1YtOnr0KNWoYVmZKyUlRQxYeHrZPrcH5R6aShqd58I5ZaVGSv32Rf/4YvCzq+z4+ljS6j33LE1GI+14830/U/Ldy71lbBvShyirBOmKuzAWmWjDB/ucnsqXg3J8U1y57rPPPrO5v/nz59OwYcNs+tHhnwIIyvnnc8NVQwACEIAABPxawNmgXP/+/WnFihUuv/fo6GjiH2QkJiYSl5e/fPkyZWZm0sWLF8nTP8Bw+c0F6YS8REBCQgLVrl2bYmNjiZfH5B9KXbhwgfg/xNB8UyAkJITi4+PFD35uJSUldO7cOfGHwvn5+W6/aATl3E6ME0AAAhCAAAQgAAEIQAACEICAHYHU1FTxZxnWQx566CFauHChdTe2FQjw8m78MwbzB4eYMjIyKDk5WXzlnwV6u+3cuZM6dOhgcxnPPfccffghqi7ZwPhoxyeffEJPPPGEzdWNHDmSZs+ebdPv7g5ngnL8c1OdTqfo0uoOeo9I47lwDpWZKOXr/yi6NgxSJ9Bx4guKltwNE6ptxdWIoHjhwyCEJHMLCym7sICyCvIoT6gSp7Tx19mOidOUDsc4BwI9X2grBB059uO48d+JkdWjKTYiXnw1lpZQjrCsca7wkZWXSaUmx8tDm4xltG7aXscnkxnh60E5vRAaPXjwoBhwrnwLvHQ2/129b5/zIcHK8+Fz7wogKOddf5wdAhCAAAQgEJQC9oJy778v/U6ivLw8mjx5stPBNQ5O8bszO3XqJH60b9+e6tatKwbkwsLCJJ8D/8N3z5499Oeff9Lq1avp559/Ju5D8z0BLonds2dPuv/++6l3797iD5WlfqhjFN6txu/EPX78OK1atYp+/PFHOnLkiO/dUBBd0bXXXkv9+vWjXr16UefOnWV/GJeVlUWbN2+mxYsX008//URc7tzV7ZFHHqF27dpJTtu4cWPi713WjUOYUVFR1t3YhgAEIAABCEAAAhCAAAQgAAEIqBZAUE41meQBDRs2JK7O161bN+ratSvFxcVJjisSgh38xkr+4J8P8c//1qxZI77hUvIAN3UiKOcmWA9Oyz9r3rFjh03Y6MyZM9S0aVMqLi724NWUn8peUE7uZ/C7d++mr776StG11h0k/BxfCN2obQ1rRlOjWtGSh527lEOHkzMk9wnvcBeCcmOl96G3SgKd3nhReJT/Pss60bHUqk49alm7LjWvVYdqR8VQfEQEVQ+RX541OTOD9iafoR2nT9Ca/bsp387XPAeUt0+YWqVrxsH/CvQa184ms2rQh1CTOi2paV3ho04rSkpsQrGRCRRTI450WukwbLGxiI4kH6BDZ/fQ7/s30IEzu/49SaXPhMwqrZ2yp1KPuk+nTZtm8fVW+eg+ffpQ69atK3eJn/P3M6705qk2ePBg+vLLL21Ot3btWvH3TzY70OF3AgjK+d0jwwVDAAIQgAAE/F9ALijHldxiYmJcdoP8DtHhw4dTjx496MYbb6xymIV/sPHRRx/R9OnTiX+QhuZ9AQ45jh8/nkaMGEFcGdCZdujQIeL/0JoyZYozh+MYJwU4FPf222/TbbfdpnoGrvQ4Y8YMev311z32Z5ErWnJAz7ohKGctgm0IQAACEIAABCAAAQhAAAIQcFYAQTln5cqPu/rqq2ncuHF03333EVeEcaZxdfutW7eKb5jlN83yz43c3RCUc7ew++dfv3493XLLLTYneuqpp+jjj/+fvfOAj6L63v4hAUKHkNBb6L33Jk2QIvqnFwGlCSggvSi9g3REpQiviBRCEC0/AABAAElEQVSRIlVBkF5Db9JrDL0FQgvZd87w27Dlzuzs7sxmN3nOx7g755a5853ZYffOc8+ZZ+f3hENJKKeX4CRL26lOHUah7Omoe8Oy9GG5fORnIcqy7GTpjlPU/6ctli6r92G/9LHaxoY+BMqMGkgfFi9N5XPlpVLZc1L6VO4tCn728gVtOHGEFuz+h+4/FS92Dh2GuXh9zh5R7UGSUE5S/aRPk4nqlmlERXOWpgLZilLihMrCRi37vhR+jlbuXkRbj663qs6BWLdMcF0oZ9WZzYbR9y2b3SluchCG06dPU/78+e3qNGjQQA7CYFcAh08RgFDOp04XBgsCIAACIAACcYOAp4Ry+fLlo3PnzukOjb8gc+QpXiUIiz0CNWrUoDlz5sirMt0dBU96FixY0N1u0F4jgSFDhtDo0aM11laudubMGfr0008pNDRUuZJOJRDK6QQS3YAACIAACIAACIAACIBAHCbAqR1XrlwpPEKOTMGpHmEgoEYAQjk1Ospl/ECb5xp4MaUow4ByS8clAwYMII5+Y6RBKGckXeP75kwJa9eutdvR5cuXZZEFZ7iIDTNacKJVKFc2b2bq8VE5qlk8p0MMEMo5RGRIBRbK/dVnCKVNnkLX/h8/j6SJm9bQltP2oioI5fRDXWfw2wwp1YvVpcEt9Rcghl7YS9NWjaB7j2/HDHrzePtzGlPoxhuj71vODK1Vq1a0ZMkSuyaclpWzVyH7lB0an3JAKOdTpwuDBQEQAAEQAIG4QcDXhXJ8Fnh1afPmzeXUnXHjrPjWUfTv358mTdIvPDuEcp45/zxZzato9QyTztHlqlevTgcOHDD0ICCUMxQvOgcBEAABEAABEAABEACBOEEgU6ZMxEInkXFECk7tCAMBNQIQyqnREZellNIBrlu3Ts4oIa7hnpfFdxwR30iDUM5Iusb3vXPnTjnNr+2eunfvLmcnsfV7attowYkjoVy1ojmo50flqXz+LJoPGUI5zah0rWiUUM48yEV7d9CsrZvMm/IrhHJWONzaMFoox4N7EvmIBszvRFduXZDHGh+Ecn5+fnTp0iUKCQmRj9nyf/yMU5R9xrIO3ns/ASk4IuEPDHAN4BrANYBrANcArgGPXQPSl0iTyB4+fKjrGKSIcqLd6OaT0q+a6tatq+uY8b3M8ffSzp0763YOzR1Jq4BwHj1wDxw+fLgZua6vt27dMmXPnt3QcygJ5YRjfvz4saH7xT3B8T0BjMAI1wCuAVwDuAZwDeAawDWAa8BbrgFJKCf83cBOnqPwlnFiHN77mZGEcsJrSIpqgutHMG8REBBgklJeCpnp5ZQi1RnOXhLKCYfbq1cvw/eN+4F794PChQsLz92rV69MQUFBsXr+JKGccGwLFy7UZVySUM5k+5et3TRT55nrTCeu3Bbu25FzyfaTdn1a7gPXq3vXqxI/SShnklKkOjo9bpXP3bHFxPsx/ymNBX7nz7EklDPx37ilA9w6R44aP4i4Z+ow5SN5X0adJ6PvW86OW8qKI8SyadMmXe6jzo4H9Z3/fCgxQ0Q5iQwMBEAABEAABEDAswS8KaLcs2fPKCwsTE5/Eh4eTkmSJJFXiOTMmZM4ZYojk8R9xCle792756gqynUg0LRpU1q+fDnxah5Hxml3r127Rrdv36bkyZMTn9NcuXJR6tSp7ZoiopwdEt0dVatWpX/++Uc1BcqjR49o7969dOzYMTpx4gQlSpSISpYsSWXKlKFy5crJn0+lgXEbrmdUyHNElFMiDz8IgAAIgAAIgAAIgAAIgICZACLKmUng1VUCiCjnHLkff/yRunTp4rARz/twGkz+u379OkVHR1NwcDBly5aNKleuLIwWY+40NiPKSUI5mjFjhnkoePVCArNmzSKOHGdrHGmI58Bj0zwZUS6hvx81rlSQvviwDOXJ5HhOXYkLIsopkTHWryWiXNSbN3Qn4gndlf7uPHlM0SYTZUydhjKnCaTgFCkpQQKWnahbt1/mUejVS3IlRJRTZ+VMqTMR5Z6+iJBTqHIa1UfPHlDyJCkpU9qslCEwMyVNnMzhbi/+d5a6z25Nf4075rCuKxWMvm85OyaOCM3fIWxNUs9R7ty56cqVK7ZF2PYRAhDK+ciJwjBBAARAAARAIC4RiC2hHH95PXTokJyO4c8//6SLFy8SC3OUTFoRSNKKEWrUqJFSFdk/f/58kqKcqdZBofsE0qVLJ6epSZMmjWJnd+/eJZ6gWrZsGV248DYMuG1lFkCyaIsnsWrVqiX/iIdQzpaS/tssfCtatKhix7t376ZmzZqRFB1OWKdIkSJyqmP+Aapkbdq0oV9//VWp2C0/hHJu4UNjEAABEAABEAABEAABEIgXBCCUixen2dCDhFBOO97y5cvTvn37VMUZW7ZsoWHDhtH+/ftVO2bBXIMGDejzzz+XF+xZVvaEUK5bt26UMWNGy93KCwFZJCdFsrfyY8N7CPDCXF6ALVqUy3Ncv//+e6wO1mjBiWXq1Zld68pCOXcPGEI5dwm61l4klLv58D7tOn+Wdl34ly7duUUPpYADJjmYk/0+ApMlp8+q1KCmpStQ4oQJ7Sv8z3P57m1qPXcGvZHEyhDKKWJyukBJKBf15jWduBJK+8/uoKOXDtCdR+H04tVzxf5L5C5H7ev0pALZlOfwufGsP8ZSz/8botiPOwVG37dcGdvBgwepbNmydk0nTpxIgwYNsvPD4RsEIJTzjfOEUYIACIAACIBAnCLgKaEcC6L4S+zp06dlcdz69esVRThqgDmS1bp16yh9+vTCahzBiiezEFVOiEc3p6NVwqtWraKuXbsSi+W0WoECBejLL78kKR0C9e3bV2sz1HOSQM2aNUlKhaLYisWNzP/169eKdbggMDCQlixZQlLKY2E9FjyywJVXhuttEMrpTRT9gQAIgAAIgAAIgAAIgEDcIwChXNw7p54+IgjltBPftm0b1ahRQ9iAxWUff/wx7dixQ1iu5uSH4YMHD45ZOOsJoZzaeFDmvQQ6depE8+bNsxsgL8zmueKXL1/alXnSYbTgxFIoN7fHh1S/bF6Hh/cm2kTHLt+i0nkyCetCKCfEYriThXKzPulISRMllsRxZ2inJJC7cu+O0/tNnyo1jW/SmoplzaHYtt9vi2jHuTMQyikScr7ALJTLm6UQjWg7g45fOkj7/91Boef3UOTLZ053+F7ROjSoxQTy9/MXtr398D/KmDaLsMxdp9H3LVfG16NHD5o5c6ZdU34OlTVrVvnZkl0hHF5PAEI5A09RQICfpJp+dwN59jxKemjH6Yrdt3RBAfRehfTSShmi3Qfv0q07L9zvNB73kCl9EmnFR+IYAvcevKR792P3C2zMYPAGBEAABOIgAU8J5fREV69ePdqwYYPiKtWOHTvSggUL9Nwl+rIgULx4cTp8+LBi2s5p06ZRnz59LFrgrTcRWLNmjTxBLRpTaGiocEWWqC77EkqrElkQpxRZrmXLlnJ6XqX2rvohlHOVHNqBAAiAAAiAAAiAAAiAQPwhAKFc/DnXRh0phHLayHIEuGvXrgnn6TijBP+G5wWz7th7771HU6ZMoQkTJtDKlSvd6Qpt4ygBntMqXbq03dGxeI6jE8a2GS04cUYo9/pNNP2++wzNXn9IipYYTfumdhTigVBOiMVwJwvl9DIWyy39/CtKlVScxnP98cM0cu0KCOX0Ai71YxbK6dgltazeSYou10Oxy5IlS9KxY/qnXzX6vqV4QCoFnOmIv5/xcwlba926NS1dutTWjW0fIAChnIEnqXH9rFSr6rtQyXsO3aMlq666vccMwUloaJ8iskjO3Nmk2Wfp2k3nFcHm9vH1NXOGpNSgdmYqUSiQiD8N/7ONW/+jDX//Z97EKwiAAAiAgM4EfFEoxwg46hWn6xTZb7/9Ri1atBAVwacDAZ6QbNy4sbAnTmPQvHlz4olQmPcR4B+QvJKW01GIjM/dihUrREWKvvbt2ysKU1mU5yhdsmLHKgUQyqnAQZHPEwhIk4oyVqtIafLnsTuWa+u20KOz5+38cIAACIAACIAACIAACNgTgFDOngk8zhGAUE4br969e9PUqVOFlUeNGkXDhw8XlsEJAnoRKFOmDB06dEjYHYssd+3aJSzzpNNowYkWodyLV1HE4rcfNoTSfw8i5MPPFpwKQjlPXgga9qWnUI53936holJkuU+Ee77/NILqThsLoZyQjmtOI4RyCRL40eTOC6hISEnhoAYOHEiTJk0SlrnjNPq+5erYWHzPKdptbefOnVStWjVbN7Z9gACEcgaeJFuhXOjxB7Rw2WW399j0w2xUo3IGq372ht6jX1detfJhQ5lAekls2OD9zFS6WForwaG5BYRyZhJ4BQEQAAFjCPiqUI6/8G7fvl0IZe/evVS5cmVhGZzuEUiVKhXduXOHAgIC7DridLccWezJkyd2ZXB4B4FSpUrJ0QBFo7l48SLlz5/f6VSpLL47d+4c5cqVy67biIgICgoKcpjG1a6hAweEcg4AodgnCSRJH0SZq1WioJJFKYGfn/AYLi1bQ/ePnRKWwQkCIAACIAACIAACIGBNAEI5ax7Ycp4AhHLamP3888/Url07YWVOgxYWFiYsgxME9CIwYsQIoSCT5yrTp0/vFQt6jRacqAnlnr54RYu2nqB5fx6mu48jrbBDKGeFwys29BbKJfL3p50DR1FC6dXWeLF7xXHf0IEh422LsO0iASOEcjyUxpXbUpcG/YSj+u6774hTkuptRt+3XB0vZ5SaP3++XfPo6Gj5WQQHCoD5FgEI5Qw8X0YJ5do0CaGKZYKtRn701EOa/+slKx827AkEBQZQvZqZqEKpYOlBkH252QOhnJkEXkEABEDAGAK+KpRLmzYt3b9/Xwjl8uXLiqkghQ3g1Eygbdu2tGjRImF9XkE8ffp0YRmc3kHgyy+/JP7hLDKeVBw5cqSoyKFPbfV4jRo1FEWtDjtWqAChnAIYuH2SQPIsmShzjcqUpnB+Yaoiy4OCUM6SBt6DAAiAAAiAAAiAgDoBbxbKBQYGUt26dals2bKUIUMGWcgRFRVFqVOnlhen8QK169ev019//UWcTtDborZz9KYKFSpQzpw55UVTnHozMjKSeD7m0qVL8uuWLVvkY1E/S86X8sI9XhxZtWpV4nPMKbiCg4PJvLCPx2D+O3/+PJ08edL5nfyvhTNCuaRJk8rR94sWLUrMI3v27HI0dxbq8Pk8cuQIrV27lniRWlyzAwcOULly5ewOKzw8nDJnzmznh+MtgcSJE1OiRIkMx/Hsmb4ZsLJkyUL169enwoULy/cuFqLxPC3fA27fvi3/nThxgtatW+cxkeQ///xD1atXt2O5ceNGYdQhu4oecBgtOBEJ5R4/e0E/bT4m/R0lfi8yCOVEVGLXp7dQjo9mWZdelDv9u8x7lkdYf/o42tT7G0sX3rtBwCihXMnc5WlCx7nCkXGWn2bNmgnL3HEafd9ydWyFChWi06dPC5s3bNjQ7XTvwo7hNJQAhHIG4jVKKJc7JAX16VLAauRzFl2kE2ehVLWCYrNRMG8q6vZZXvL348te3SCUU+eDUhAAARBwl4CvCuX4uDlyWcqUKe0Q3Lp1S54stSuAw20CGzZskCfDbDviibAcOXLQy5cvbYuw7UUEJk6cSAMGDBCO6LPPPiNeBe6KffDBB/Tnn38Km/I+Bw0aJCxz1QmhnKvk0M6bCKTMmV0WyKXOl1vzsCCU04wKFUEABEAABEAABOI4gTp16hALIByZvyB6Crd58+aNo6ZW5a9fvyYWQrlr/Ntp8ODBVKVKFVIam+0++Pf2smXLaPTo0YoLBm3bOLPN4+G+bY0FeiyGM1uCBAnoww8/pP79+8siNbNf6fXFixe0YMECmjx5Ml25ckWpmmY/75sXf3EaxWTJkmlux0K52bNn0+LFi8lZwZAWoRyLhpgJR1RjAaQj27x5sxz1hUV8ccVu3LhBHDnO1v7++2+qXbu2rTtWt3mOokmTJi6NYcyYMcSiAb1MKQqaXv2b+2EBrrvZH1igyp8/XkBbokQJc9cOXw8fPkzjxo2jVatWOazragUeG0cPSpIkiV0Xw4YNE97f7Cp6wGG04MRSKFe3dG7KmTGQfpGiyHE0OTWDUE6NTuyUGSGUm9byM6qS11rPYD66xrMn0+ru/c2beHWTgFFCucxB2Whh3/XC0RklCjb6viU8GA1OPykTx8OHD+VFErbV+Xsnfy+D+RYBCOUMPF9GCeV4yGWKp6XqlTIQa772HLpL+w7fl1JWmQw8Gt/vunzJIGrXPKfdgbx5Y5ImCPij8M4glHvHAu9AAARAwAgCviqU43SOvCpXZPv376eKFSuKiuBzgwBPOPHEmmi165w5c6hr165u9I6mniAwd+5c6ty5s3BXtWrVom3btgnLHDk5Zeu///4rrMZRBPghlp4GoZyeNNFXbBDIVLUCZWvwvtO7hlDOaWRoAAIgAAIgAAIgEEcJqC3WMeKQOdqb6Lew1n3lzp2bZsyY4VZkI34gyFHAZ86cqWuEuW+++YZYAGRrLJTjiHdsHC2Ko0NVqlTJtprDbRYlfvrpp/Trr786rCuqwL9Vx44dS+XLlxcVa/Y9fvxYFjlNmTKFWNilxRwJ5VjwyFFcOCqgM/bq1Stq2rSpzNSZdt5al8WcHFXM1iyvIduy2NpeuHAh8UJBV0zvTAq+IpRr2bIljR8/nkJCQlzBJrfhiG88b2iEQJTFszt27BCOjf+tYHGqN5jRghNLoZwzxwuhnDO0PFPXCKHc8q59KFc6+/t0tJR6teqEobT367GeObh4sBejhHKl8lSk8R1+FBL8/vvvZTGzsNANp9H3LTeGRizG5++Itnbo0CFhlFvbetj2LgKsDoK6yqBzYqRQzqAhx+lubYVyLJDbe+ge/bk9nPpKEfrSBiaOOX4I5WJQ4A0IgAAIGELAV4VyLLzhFCQi46hYrk56ifqD7y2BkiVLyqlCRDw4ZYzS+RDVhy92CKxYsUKejBftPV++fHThwgVRkUMfiyg5xQZHGLA1jhyQK1cuW7db2xDKuYUPjb2AQNY61SlzzSqKI3ly8SqlyhNiVw6hnB0SOEAABEAABEAABOIpAV8SyhUrVkx+mMcpQvUwfmjZsWNHabF+tB7dkSOhHKc3ZaFJkSJFXN7fwIEDadKkSU6159+Xo0aNoiFDhjjVzlHlXr16yaJFR/W4XE0ox+Pjc+GqgJIj8terV49YQOTrduzYMSpevLjdYURERAijvdhV9KADQjntsBMmTEjz5s3TbY6VFzzzvZvTEOtpQ4cOle8Vtn1yymoW+XK0OW8wowUnEMp5w1nWZwx6C+WSJkpM2weOlAL+2M/b/vfoAX08axKFDpuoz+DRCxkllGtRrQN1+OArIeGvvvpKXkghLHTDafR9y42hyYs8+DusrfECDY7wy99BYL5DAEI5A88VhHIGwnWha7NQzlIg9+jx2/C/owcUg1DOBaZoAgIgAAKuEvBVodySJUuoVatWwsP++uuv5ZWOwkI4XSbAaUREqTmfP39OnMaBU9HAvJsAp0flSUmRuZuK48GDB8I0M/zjlNPi8Ip5vQxCOb1Iop/YIqAklHt09jyFbdtDz26EUbF+X1CS4LRWQ4RQzgoHNkAABEAABEAABOIxAV8RypUqVYo4yjaLNfQ0TsXapk0bp1PIisagJpRjzhyZw93FT84K5XgxFj+cbdGihWjIbvn0EMpxJLABAwY4lQJWNOhLly5RoUKFdP29LNqP0T61ObrWrVvT0qVLjR6C5v4hlNOGKnny5MSLLVnMqadxpgpe/HzgwAHdulWKKsSZDwoWLKjbftztyGjBCYRy7p4h72mvt1CuSenyNKh+I+EB7rt0nnouWQChnJCOa04jhHKJEwbQjG6/UK5M+YWDMiqIgdH3LeHBaHQ2bNiQ1q5dK6zN/3bxcxCY7xCAUM7Fc8UC6JQpElGypP707Nkbehr5Wgo9bt2ZrwvlUiZPREmS+NG9B68Uw6onTJiAggID6MGjV9KDan1Wk1lT1G8rTerEVLNyetq25w6ZBXLm3iGUM5PAKwiAAAh4hoAvCuV4hcz06dOFgFiUw6u1z5w5IyyH03UC3377LfXr18+ug6NHjxJP/lsar5Tn1Co84ZszZ04KCQkhPz/pu4y0evTu3btympOtW7fKDwzu379v2RTvDSSwatUqatRIPDGSI0cOun79ukt79/f3pxcvXhCvNhZZgQIF6Ny5c6Iil3wQyrmEDY28iIClUI5XuT88eZb++2cPRYbfjhklhHIxKPAGBEAABEAABEAABOwIeFooJ/rdazcoG0fSpEnp5MmTxGlX1YyFIyxGu3PnjvQMIIlcn+c1HFmfPn1o2rRpjqo5LFcTyu3evZtYWOauOSuU4zStLLByZLxgj89NeHg4cWrVlClTEs9HFC1aVF7QJ2qvh1BO1K/Zx3MenIqUzztfA46sb9++NHXqVEfVvLqco/6NHj1aOEbmwWIlb5n7gVBOeJqsnBwtcf369VS/fn0rv2iD54JOnz5NN2/elOf/eP4nceJ3GaNEbXjuiSMQ6hHpjeeh+LPPCzRtzdsyjhgtOIFQzvYK8N1tPYVyRbNkpzmfdqFE0tytyMauX0lrjh6CUE4Ex0WfEUK5/s3G0PslGwpH9PDpfcqeKYSePn0qLHfHafR9y52xccp3/r4lMk4XzsE0YL5DAEI5J89V4fypqWr5dFQoX2ry92d8b42jlF24HEEHj92nQ8ceSCHITeSOUI7FZ7WrZTR3r/r6LDKKNvz9n7xP1Yo2hTUqZ6AM6ZLEeHcfuEs3wyMpX+5U1LxhNsqU4e0Pqpcvo2nD1jDatvt2jBiQRXSNG2Sl0sXTkr9fAoqKMtGlqxG0cPkVinjqe5FdIJSLuQzwBgRAAAQ8QsCXhHKczqJ9+/Y0e/ZsRUHOnDlzqGvXrh5hF992ohSNjCeweSU7G08E848QntDmCX5HxqliNm7cKLfhBwgwYwn89NNP1KFDB+FOKleuTHv37hWWOXKyyO7q1auK1Ro0aCCfZ8UKThZAKOckMFT3OgKZqlciFsvdP3qS/tu+l17ctRcMQyjndacNAwIBEAABEAABEPAyAizmUDNOGRoWFiaswmKO8+fPC8tETl7c4KxxqtH+/fsrNuOxcfnq1avlhUeWFfPkyUMshOP5DaXjjIyMlNOhXrlyxbKp0++VhHIsYmHxia3ohaOF8/zApk2baP/+/bLAjxct8nwAC6KYLUc2sUzV6oxQrnPnzjR37lzV4zh48CDxYj5OCctCQ1tj8Uz58uWpWbNm9Pnnn1sJ1owQyvF8xvDhwyk0NFReGMjj4QVlfB6HDRumKvpjkZElK9tj8YXtxo0b08qVKxWHyvMFPXr0kMVXipU8VBAcHEwpUqRQ3duGDRvkhZ+2lXr37q24cNe2rpZtnjfTIqZ01Bdf5zwfKjIWk6ZJk4b4fqHV+DjVxJt8P/zxxx9p1qxZ8n2UP/9m488eL5qdOHGifB8w+21fly9fTi1btrR1O73N93lOkSwyZ+47ovZ6+4wWnEAop/cZi73+9BLKlc2Zh0Z+3JzSpUwlPJhLd25R67kzKdoUDaGckJBrTj2FcskCktMnNbtQ06qfKg5mxprR1KvRMMVydwqMvm+5MzZuy4tMeIGErXEkWy0LLmzbYTv2CEAop5F9kgA/atUohMpIwjBHdv3mM1q04gpVLBNMtaq+E7uFHn9AC5dddtRcLs+RNTkN+LKgprpcqc+II8SCNmesZ6d8lF8SxZlt7uJLUth0E3Vrl4dI8Hv/l9+v0v7D9yhpEn8aKI0tXbD9g+iw8Oc0Zc5Z4Vi4XfFCacy70+31VVQ0nTjzSBbrudophHKukkM7EAABEHCNgC8I5XhCp3nz5jR48GA5MpnSkUZERFDevHkVV5IotYNfG4GzZ8/KE962tXnl8NixY6lGjRrEEcv4fDlrLJibP3++PHGqZ4pOZ8cR1+tPmTJFftgiOk5OafPbb7+Jihz6qlWrRtu3b1es17ZtW1q8eLFiubMFEMo5S8w76ydMmoQyVCkvP/i7uXm7dw7SoFElkCJsJk6Vgl4+sn+oZ94lhHJmEngFARAAARAAARAAAdcIqAko8ufP75RQztkR5MuXT450z2IpkfHvJxYXPXz4UFQc4+OITvw7OyAgIMZn+WbNmjWKUcMt66m9VxLKidrwWDjlKKcMVTOOKM8LHTnKGJ8HrYIVFtlxhDilhXfPnz+nbt260aJFixQz79iOK3PmzDRy5Ejq1KmTXKS3UI6ZtGvXTsp29Mx21zHbLBLj6H9K1wML5Vgw56uWIUMGunDhghzRT+0YOErZggUL6K+//nJKuKXWpxFlfA2WKFHCrmu9hXJ2O3DBwdfOvn37FMV/fO199913mnvmSG8sRLUVyJo74GhwHTt2JE53qmYs8OWMIBMmTFC8f/E8otpcklr/5jKOvnn8+HHzptUrf+Z5wai3mNGCEwjlvOVMuz8Od4RyAQkTUemQXNSxak0qljWH6mB6SClX90upV9lCh01UrYtC7QT0EMoFpUpH9co2oUaVPqEUSd/pR2xHcfX2Reo2qxn9OfaYbZEu20bft9wdpNLzKv43qnbt2u52j/YeJAChnAbYHDHty/Z5KX8e5ZuCbTfPX7yhazeeUYG879p4u1Buzaab9EGNTLIQzvZ4ePvBw1c0cupJ6tQ6DxUtmFpURfaZBXW2FTKmT0JDexexdeuyPfn7s3RF4u2qQSjnKjm0AwEQAAHXCHiDUI5XC1evXt3qADhdBkep4r/UqZX/rTM34jDLLJ7hyRyYMQQ4jUKWLFnsOufJb16hyukrOOqfO7Zt2zZ5kl+0ItydftH2LQGOVsBRDUTGaXVZSOeK8TXAk91KxlEQlFY3K7VR80Mop0bH+8vMArmMlcuRf5IAur3nEF1b95f3D9zDI4RQzsPAsTsQAAEQAAEQAIE4RyA2hXIcTYkFZSI7d+4cVahQQXPawU8++URx4RFHcsqePbtiRCXR/m19WoVyvHiRRS/OWPLkyYlTi/7zzz+0a9cuh005VSKLzkTGKcUaNmzosrCmadOm8u9W/l2s9fcpR6ri60jJOOMBC5G0RBzk6CZKEbS6d+8uZ09Q2o8v+PkYOMKYFmPB45YtW2jt2rXydeFMdEct/btbx1eEcoGBgfI8KEcuFBkLHHi+xhlj8e3HH38sbHLs2DHihZLOzNnVq1dPMcMAXwN16tQR7kur8/3335evJVF9FiNzxE5vMaMFJxDKecuZdn8cakK5ktlzUuPS5a12ktg/IWWQnp9kSh1IaZOrR8zkhlHSd4fxG1fT2mOhMf1AKBeDwu03WoRyn9XpQRkDM1vtK2XS1JRB8qVPk5kCEokXSFg2uBx+joYu6kH3Ht+mzeNPWBbp9t7o+5a7A92zZw9VqlTJrhsWUIsE73YV4fAaAhDKaTgVjeplpfffexcZzrIJi8euhT2jpAH+lCskBSVO5GdZbPXe24VyloO9e/+lLJhLkTyhpZuePosis08KwiJFoIuWHk5bH/OZ849p9sILVu14A0I5OyRwgAAIgEC8JeANQjmOJMarEV21U6dOyZOlaqkfXe0b7d4RePz4MaVK9W7hgblk6NChxFHllFa4m+tpfeUfODzxZpm6QWtb1FMnwIJUfkAhMndWWvEKYD5nSsYpg3j1vF4GoZxeJD3bj61Azrx3COXMJKxfIZSz5oEtEAABEAABEAABEHCWQGwJ5Tj14I0bNyhjRvFzjJo1ayr+LlM6xnXr1tGHH34oLHZFwGbZkRah3Pjx4+nrr7+2bKb7e16Yx2lklRbgffHFF/TDDz+4td+QkBDiRYBRUVGa+lETyrFwj0WKjqICmnfEqWA5Va3IOI0lR8rzZeMognv37pVT3jp7HPfv35fb7tixg1asWEEctSw2zReEcsybI/SxEE1khw4dovfee88urbOortnHWTr+/fdf4r5tjcWNpUuXJo7e46ypiURLlixJLMBz1Vq1akVLliwRNuc5ML6mvMWMFpxAKOctZ9r9cagJ5RoUK0UjpHSqrtqTF89p0O+/0qErF626gFDOCodbG1qEcrO7L6c8mQu4vJ+D53bSuGWD6PnLt4GL4qtQjgX3vIjC1vj7myjgg209bHsPAQjlHJwLFoWNHljMTgB3PSyS5v96ie4/fBnTQ0qpbp1qmahmlQzC1KXOCOUS+ieQQkaLo6MEpQmg3l3yx+yX3+iRetXc4Z5D92jJqquUNVMyGti9kPQF1Vzy7vXRo1c0UYriFvE0ivp1K0Ah2ZLHFEZJqVC/GnokZtv8xkih3LfSWK4iopwZNV5BAARAwOsJ+KpQjlfrbt68WZ4k5YkhiKqMvdQ4ZQJPJIsmy9T2zJHmOH0Ip2Xh6ICcEkIphYplP8OHD6dRo0ZZuvBeBwIpUqSQoxYopXspVaqUnObGmV05SrvKfZnT8zrTr1pdCOXU6HhfmZJAzjxSCOXMJKxfIZSz5oEtEAABEAABEAABEHCWQGwJ5ThCEqeWFBkvXGKhnLPGUTFYvCMy/s3Nv7VdNUdCORYx5cqVy6koUq6MhcV4gwYNEjblhZGczpbnGDxpakI5jsjOkdmdsdDQUFlsZNtm9+7dVLVqVVu3z20XLVqUePEjZ4lw1Xi+b+vWrXLWAo4GxgItT5svCOXGjh2rKF69c+eOfJ2xKNQZ4wiJLEgV2Zdffknff/+9qMihj0XDLLBLkyaNXd0RI0bIqZHtCjQ6evbsSTNmzBDW5uuRF1d7i0Eo5y1nwvvHYYRQ7uKdW/R76D7adPIoRb56ZQcBQjk7JC47jBLKvY56RbtObaF1+3+jM9etBcbxVSindF/l74tKKcRdPrFoaCgBCOUc4G1YJwvVldKRWhqn+Jy94DxxelWRlS0eRG2bhxCnbLU0Z4Rylu1s36cLCqAR/YpaufUSyl2+9pSmzz1Hb6JNcv+9Py9AeXJah0x988ZEk2afpZvhkXKdimWCqU2TEKvx9B1xhF68lELOWRhH28uR9Z2gzqLIrbevXkfTtZuup13lnSP1qlunAI1BAARAwGkCviqU4xW7vXv3puXLlzu1OtJpQGggE2CBVUREhGYaJ06ckCfXeBL45ct3ixlYoFW4cGE5HQevbFUyFuXxgwCe8IfpS4DTE5ctW1bY6bJly4hX4zpj/KCHV+mqmd7RByCUU6PtPWWOBHLmkUIoZyZh/QqhnDUPbIEACIAACIAACICAswRiSyg3bNgwReHHZ599Rpxe1BXjNFLFihWza8rCIhagOJMO0bITR0K5gQMH0qRJkyybGPKe5xFY2CIyzkKwYMECUZGhPiWhHD+AzZkzJ4WFhTm1/4ULFxJfA7Z24cIFWQho6/fF7dy5c8sRvsqVK+f28DmzweLFi4mvUX7vKfN2oVyTJk3kyHu8qNXWeD6tVq1atHPnTtsih9t8PWfObJ0KkBtdvnyZ+Ly6YxwNsmvXrnZdcJTFihUr2vm1OkaPHi0vzhTV52hC/Bn2FlMSdLDf2RS5omNCRDkRFd/0GSGUm73tL/rj6EF6GCl+hg+hnH7XilFCufM3T9MvW3+gQ+f3SCnfrXUf8VUoN3XqVPkZoejscXryR48eiYrg80ICEMqpnJQkUjrVMYOKySlIzdVeSuKvIROOU6SCSM5cr3qlDNSsYTbzpvzq7UK5qCgTjZtxmm7fexEz7v+T0s7Wtkk7u+Hv/2jj1ndf9DKlT0JDeluvHhs26aRVtL2YDr30DYRyXnpiMCwQAIE4S8BXhXLmE8Irm3myZeTIkZpTZ5jb4lU7gQwZMtCtW7c0NWCxFU9iR0a+FfKLGnEqGp5oZ7Gjks2bN48+//xzpWL4XSTQqVMnYrYi48iMLELbuHGjqNjOx6v9WQTnyKZPn656rh21ty2HUM6WiHdtaxXImUcNoZyZhPUrhHLWPLAFAiAAAiAAAiAAAs4SiC2h3B9//CH/rrIdb3R0NPFv63v37tkWadpWE4S4k2JQTSjH8wAskFH7fa9p8A4qsdDvwYMHJBL+XLx4kQoWLBgrcz5KQjkW9RUvXtzBUdkXK0UBCw8PFwqU7HvwDQ+nz+UsARxxj+d/3DUWajVv3pwOHz7sblea2nuzUI4Xn7K4jBe0iowjrM2aNUtUpOrjqJGcDUJkc+fOpS5duoiKNPtatGhBPF9oa3xf5GNxNXIgR7lTSlucNGlSr1pcDaGc7dnHthIBI4RyvK9Xb6Jo65mTNG3zejvBHIRySmfDeb9RQjnzSMIf3KTlO36iTYdWmV0UX4VyQ4cOVcxKlCdPHsV/12LA4Y3XEIBQTuVUlC8ZRO2a57SqsX3vbVqx7oaVT2njy/Z5qVC+1DHF3i6U27HvDv229nrMePlNjcoZqOmH7wR/T59F0TeSUJBFdWZLlsSfvh1e0rwpv3LEOXejvFl1aPAGhHIGA0b3IAACIGBDwNeFcubD+fPPP6lZs2b09OlTswuvOhJIly4dceoGRzZz5kz66quvHFWLKR8zZoy8MjjGYfGGJ+KzZs1KHD0Qph8Bnijk9Btp06YVdsrRCFh4yg9heMJSZMmTJ5dX8/NEtRabPHky9e/fX0tVTXUglNOEyeOVnBXIeXyAOu3w4KAxOvWk3g2Ecup8UAoCIAACIAACIAACjgjEllDuxo0b8m9Z2/G5myJVLaVrnz59aNq0aba71LStJpRjsdPw4cM19eNOpQYNGtD69euFXfBDUJ47iA1TEsrxWBs2bOj0kDh95XfffWfXjuey3ElXatehlzj4M8gR9HgxpbsRyThbQd++fYnTgxpt3iqUY0EpZwnImzevEIE7kcnatWunGO2Sma9YsSJmn2ZBq/mVC0TvLX18LSxatCimD8s3+fPnp/Pnz1u6NL9nUWD37t2F9fkz5U3zxBDKCU8TnAICRgnlzLu6+fA+9VyygG48uG92EYRyMSjcfmO0UM48wNV7FtOcjZOl6HKmeCuU4/TdSt9TOfLv1atXzbjw6uUEIJRTOUEN3s9M9WtZhPyVtGEjppyku/ffpfJSaU6N62elWlUzxlTxdqHc6o036e9d1lFbbNOqht9+TmOmn445Jn7jJ6WYnTW2tJVv8vdniVPU+opBKOcrZwrjBAEQiCsEvEEoFxAQQLza1NJSpUpFvOqD/3gyjdN0VqlSxbKK3XteWcqTq7dv37Yrg8M9AiyMcjS5xGkw+AeIM8I2Fmtdv36duH+Rffrpp4oTaaL68GkjwJPLLF5Ts+3bt9PatWvp2LFjxCl++DNaunRpKlOmDLVs2VJe0a/W3rKMf7DyAxa9DEI5vUjq109A2jRUoEs7CkidSr9OvbQnCOW89MRgWCAAAiAAAiAAAiBgQyA2hHI8v/HixbssMZZDWrVqFXHKRFcte/bsdO3aNWFzjqrEIixXTE0o98knn8hpNF3p15k2Q4YMkRdridp4agyifSsJ5TizwRdffCFqoupr3LgxrVy50q4Op8u0nRezq+TDDhZM1ahRg1q3bi2nBQ0JCXH5aJg78zfSvFEo5+fnR+vWraP69esLDz00NJSqVq2qeP8RNrJw8sLXHj16WHg89/b999+nrVu3urRDtVTXOXLkkOccXerYgEYQyhkANY52qSaU85fuBQE2kToT+SekLIFpKVvaIMoaGEz5MmSi6vkLSZoBP0VCnIK1z7L/R6fC3gYkglBOEZXTBVqEcgGJkpBfAuvzkzp5IGUOyi79ZZP/yuarQtnT51Ld/86Tm2nSim9ow+hQ1XquFhp933J1XOZ2av92cbTSZ898Rx9jPqb4+gqhnMqZ/1SKJldOiipntpvhz2n8TGuRmLlM9BoXhHKli6WlDq3e3RBFQjk+9pljSpO/P19Obw1COTMJvIIACIAACIgIeINQTjQukY9FcBMnTiROM6BkBw4coAoVKigVw+8iAZ7UVIouZu7S1ZXmvOq9V69e5m6sXn/88UfFFApWFbHhFAE+nzwJyRPVehkLVBMnTkyBgYF2XboT3cCuM8kBoZyISuz7EFFO33OAiHL68kRvIAACIAACIAAC8Y9AbAjl0qdPr7h4b/z48fT111+7fCL4dxw/8OMo4bb266+/Ups2bWzdmrbVhHKVKlWiffv2aerHnUpq8wKVK1emvXv3utO9y22VhHKDBw+mCRMmON3vhx9+KIudbBu+fv1a/j1t64+r2yxg4vPKf7wwtkiRIpoP9f79+5QvXz45Va/mRk5W9EahnFrqZc4AwYsaOZqlq7ZkyRJq1aqVq83dasf3Lr6HuWKcdpWFwiIrVaoU8bn0FjNacJKl7VSXDjVbcCraN7WjsO3SHaeo/09bhGXsDPulj2IZClwnoCaU09pr7vQZqEetelQ5TwHFJpGvXtL/zZokp2GFUE4Rk9MFWoRyWjploWPdMo2pXa1uFJgyWLHJ+gMrqGEFbVlfFDtRKDD6vqWwW83uxYsXEy+osDVO550sWTJbN7a9mACEcionp2+XApQrJEVMjRNnHtGcXy7GbDt6E5+EcjNGl6aECdWFcv5S5LnAwMSOsDldHvU6mh49ee10O8sGiChnSQPvQQAEQMB4Ar4klGMa/v7+NGjQINW0G40aNaI1a9YYDy+e7YFToYom5BlDREQE8er2R48eOU2FJ0iVwmBzNLOSJa3Tyju9AzQQEuC0tjt37pSjAAorOOnkNKycQoYfDNla586daf78+bZul7chlHMZnUcaOiuYu73nEF1b95dHxuZLO4FQzpfOFsYKAiAAAiAAAiDgjQRiQyjHUfEvXLggxMGRvadOdU3IYO7w5s2blCVLFvNmzKurqUC5AzWhXObMmSk8PDxmP0a9+fnnn4lTP4rMU2MQ7VtJKOdq9HsI5USUiUJCQojnJ/mPs0nw3J+a8dyDkdHPvE0ox/OcHImQxbK2xtEIOSLbjh07bIuc2v7zzz/pgw8+cKqNXpX5s//LL7+41F3Tpk2t0sJaduJOpDrLfvR6b7TgBEI5vc5U7Pejh1DOfBQVcuejKS3aUWIp6pzIlhzYTdM2r0fqVREcF316CeXMu0+SOCl99X9DqWaJBmaX1eub6DeUP19+unTpkpVfjw2j71vujnHTpk1Ut25du244gxE/c4L5DgH+hiMlFIWJCIwbXJxSp3qXkm3H3jv027rroqpCH4Ry1qElM6ZPQkN7a1+lI4Sq4Jww6wzd+C9SodSxG0I5x4xQAwRAAAT0JOBrQjnzsf/222/UrFkz86bV66lTp6h48eIOI6BZNcKGQwL37t2joKB3EX4tG+zevVtO8WDpc+Y9p23ldLu2Ft9WVdsev9HbGTNmpA0bNhCvsnXVeFKWU5/MmzePlMSUnKp1+fLlru7Crh2EcnZIvNKhVTAHoZz49EEoJ+YCLwiAAAiAAAiAAAhoJRAbQrnSpUsTp0AUWdeuXWnOnDmiIs2+f//9l/Lnz29XnxdBVatWzc6vxaEklOMUshyNw2Qy/rEVp5RkEZmtvXz5Ul6w54kx2O6bt5WEcpxCdOnSpaImqj4loVxcT72qCsWmkK9vXmjHgjkle/PmjTzvd/q09qxTSn2J/N4klCtUqBDt37+fUqZMKRoq9ezZk2bNmiUsc8Z58OBBKlu2rDNNdKvrjlCO73vbt28XjoUXdK5YsUJYFhtOowUnEMrFxlk1Zp96CuV4hM3LVqL+dT8SDvaVNK/baPa3tLGX6xFvhR3HY6feQjlGGZAogGZ+sYRCMuQRknUnsrCww/85jb5vqe1bSxlnlypXrpxd1cOHD8uRVu0K4PBaAhDKqZyaaSNLSaGn3+VqXr85jDb9o30lE4RynhPKuZvqFUI5lQ8CikAABEDAAAK+KpRjURVPXOXK9S4tuSWeJk2a0KpVqyxdeO8mgWvXrslR40TdcJjrtm3bioo0+Xhykyf/RJYiRQo5vYyoDD73CTDfYcOGyauxkyRJ4lSHHEGwRYsWtHnzZuJV/mFhYcL2vLLrr7/0ixgGoZwQs9c6HQnmIJQTnzoI5cRc4AUBEAABEAABEAABrQRiQyhXtWpVOXK3aIydOnWin376SVSk2ccLAwsXLmxX350HgkpCufPnzwtFeXY718Hxzz//UPXq1e164gh62bJls/N7ygGhnKdIW++HI6f16tVLNQIjX7fjxo2zbqjTlrcI5VKnTk0sYONUsyLjSIyfffaZqMhp39mzZ6lAAXGKRiUhmtM7ETRgQW6XLl2Io/+4YjyXqCSY5LSsP/74oyvdGtLGaMEJhHKGnLZY6VRvoRwfxKRmbahGAXEAnWUH91Cr8sri5FiB4MM7NUIoxziyp89FsySxHEeYs7Xo6GjKmzcvXb582bbIrW2j71tuDU5qfPHiRcqdO7ddNxwltV69enZ+OLyXAIRyKudmZL+iFBwUEFNj667btGrjjZhtR28glINQztE1gnIQAAEQiK8EfFUox+eLJ4MWLlwoPHXjx4+nr7/GSighHBed+/btowoVKghbjxkzhoYOHSos0+JUS/HAk+I8OQ4zlgCnYuWJ6Pr161PBggVVd8apcmfOnCk/5Hny5Ilcl1NabNmyRdiO06iw0FIvg1BOL5Ke7UdJMAehnPg8QCgn5gIvCIAACIAACIAACGglEBtCuTJlytChQ4eEQxw0aBBNnDhRWKbVeevWLcqQIYNddXeivCsJ5TgynqciTHHq2AYN7FOKPX/+XI5qZ3fAHnJAKOch0Aq74cj0HBVMZHqKxGz79wahHIsF165dK4y0yOPlzycLc1lopodxfxwR09bu379PwcHBtm6v2U6XLh3duXNHOB5eFDp69GhhWWw4jRacQCgXG2fVmH0aIZTLEpiW1nQfIBzw8RvXqET2EGEZnM4TMEooxyP5ouEg+rhiK+GgOPvS77//Lixz1Wn0fcvVcZnbKWUp4nTeHK0U5jsEIJRTOVe9OuenvLnehRY+fOIBLViqXRULoZy1UC55soRUpXw6FeKuFb16FU37Qu/Si5fRrnUgtUJEOZfRoSEIgAAIuETAl4VyRYsWpRMnTgiPm0PrK02mCRvA6ZAA/8Bo06aNsF7nzp3l1BjCQg1OXlXfoUMHYU0+z7xqHuY5AtmzZ6cSJUoQTzjyH6f44InHK1euyH9nzpwhTnViaT169JDFc5Y+fs9COl4FradBKKcnTc/3ZSuYg1BOfA4glBNzgRcEQAAEQAAEQAAEtBKIDaEcR/PgSGwimz59OvXu3VtUpMnn7+9Pr169Ij8/P7v6GzZsUBTU2FW2cXiDUG7RokWKUeozZsxIt2/fthm1ZzYhlPMMZ6W9ZMmShc6dO0fJkye3q8KLOStVqmTn18PhDUK5kSNHytH/RcfD8zMsyr1xQ3swEVE/lj7OFFC7dm1Ll/w+IiKCOKuHtxoLCu/evUtBQUF2Q+RMI5xxxFvMaMEJhHLecqbdH4cRQjke1bb+wyllkqR2A3zw7CkFpXinwbCrAIdTBIwUytUu9RH1ayoWAA8ePJgmTJjg1FgdVTb6vuVo/2rlHEmOI8qJjAM6cGAHmO8QgFBO5Vy1a5aTypd690Xn8tWnNGXOvyotrIsglLMWylnT8a4tCOW863xgNCAAAnGfgC8L5RImTEhPnz6lgIB3UWfNZ+zYsWNUsmRJ8yZedSDAKzF5ok5k7grlFixYQO3btxd1TUWKFFFMoyBsAGesEOD0u5988ondvvfu3UuVK1e287vjgFDOHXre09YsmOOJ7Zubt3vPwLxkJBDKecmJwDBAAARAAARAAAR8lkBsCOU42htHfRMZR8dq2bKlqEiTT+14lixZIvw9pqVjbxDKsYjwq6++Eg63YsWKtH//fmGZ0U4I5Ywm7Lh/TvtZrVo1u4pGRjqLbaEcz9WyyIt/K9taVFQUcUT/HTt22Ba5tb1s2TJq0aKFXR+vX7+W511NJpNdmbc4Vq9eTczM1vjzy2JLbzGjBScQynnLmXZ/HEYJ5X5o25nKhOQWDpAFsSyMhblPwEihXM6M+ejHniuEg+TnKx07dhSWueo0+r7l6ri4HT+H4OcRIuPvDTt37hQVweelBPgbj/d+04hlaA3rZKG6NTLFjIIjlw0ed0xz5DII5SCUi7l48AYEQAAEQMCKgC8L5fhALl++TDlz5rQ6Jt7w9hWPdgP2AUfr1q3p119/FY7U3dSrf/31F9WpU0fYN6cEDQsLE5bB6R0EEiVKJEecS5Mmjd2A5syZQ127drXzu+OAUM4demjrKwQglPOVM4VxggAIgAAIgAAIeCsBNWFZ/vz5FSO/uXM8SZIkIU4XKjIWtlSvXl1UpMlXqlQpOnz4sLDujz/+SN26dROWOXJ6g1BObWEez0UsXbrU0WEYUg6hnCFYnep07ty5xIszRZY2bVp6+PChqMgtX2wK5QoUKEAHDx6UI/uLDoIFpTNnzhQVueWbPXs2ffHFF8I+eN716tWrwjJvcPbq1YumTZsmHEq2bNno5s2bwjJPO40WnEAo5+kzatz+jBLKDf+oGX1YvLRw4BxwgAMPwNwnYKRQLmniZLRmxD7hIN39ninq1Oj7lmifWn38byFnuLE1TknOzyhevnxpW4RtLyYAoZzKyalUJpg+aRJiVWPZmmu068BdK59ogxdd9O1akHJmfxeeOfT4A1q4THvqVlG/7EsXFEAj+hW1Ku4z4oj04XMu9WjPTvkof+534YtXb7xJf++yXnlWulha6tAqV8y+wm8/pzHTT8dsm9/MGF2aEibky+mtTf7+LF25AaGcmQdeQQAEQAAErAn4slCOxTkcUS5x4sTWByVt8YpHkd+uIhyaCZQtW1aerBM14NU7bdu2FRVp8p0+fZoKFSokrMspNiIjI4VlcHoHAU7PwWk6RMYr2XhFm54GoZyeNNGXtxKAUM5bzwzGBQIgAAIgAAIg4CsEYkMox2zCw8OJ04XaGqdkZYGeq9agQQNav369sPnAgQNp0qRJwjJHTm8QyinNTfHYeXzjxo1zdBiGlPuSUE4tmqEITqNGjWjNmjWiIq/yqaUgzZEjB12/fl338caWUI4jOrFITuk+8fPPP9Nnn32m+/Fyh126dCEW3Irsww8/JE7v7K3GAp8jR44Ih9e0aVNauXKlsMzTTqMFJxDKefqMGrc/o4Ryc9p9TqVyvNMZWB5BbEZvtRxHXHhvpFAud6b89H2P34SYDhw4QBUqVBCWueo0+r7l6ri4HR9vuXLl7LowQjBotxM4dCcAoZwK0pTJE9KYQcWtBGA3/oukCbPOqLR6W1S/VmZq8H5mq3oQylnh8KoNpF71qtOBwYAACMQDAkqTkY8ePaLAwECvJsApOU+ePCkc44MHDygo6F3admElDc7y5csTTzg7Mk6torTy0lFbXylnYSKntkiZMqXdkPfs2UNVqlSx82t1PHnyRNgvr/zhFfnu2ujRo6lw4cIOu5k/fz5t3LjRYT1UsCbAKYSaN29u7ZS2WLDKD4j486ineZNQjtPK9u3b1+Hh8Qrmnj17OqyHCiBgJgChnJkEXkEABEAABEAABEDANQL8W4RFayIrXbq0orBCVN8ZHwtK6tevb9ckOjqaQkJC6MaNG3ZlWhzffvst9evXT1iVFy/9/fffwjJHTm8QygUHB9Pdu+KgCGfPniWe/2F+njYI5TxN3H5/v/zyC7Vp08a+QPLwdcPzVHpbbAjlOM0qCxd5vkNkoaGhVLVqVeJIOUZYwYIF6cwZ8fNWnlPjqI/ean5+fvJ1IMpywPfNAQMGeMXQjRac+LpQjiND1ahRw+G5Wrt2LTHLuGxGCeW29h9OqZIkFaLjf2d5Ibs7xovd+Z6txThSqBH3by37NrqOkUK590s2pP7NxggPgb8H8vdBPc3o+5arYw0ICCB+niQKlDFq1CgaPny4q12jXSwRgFDOAfi2TUOoQulgq1or1l2n7XvvWPnMGxxJ7qM6WalOXNLyfAAAQABJREFUdfvVWxDKmSl53yuEct53TjAiEACBuE3Al4VyaqlAeSUhT3y7a0p8bPu9dOkS5cmTx9Yd57ZXr15NzMTWOLJf9uzZXUp5wSkcOIWuyHgikCPZuWvbt2+natWqOeyGJ2W+++47h/VQ4R0BTv/D54kndW2NHxDxymO9zZuEciwQZKGgI+OHO0pREx21RXn8JAChXPw87zhqEAABEAABEAAB/QjwIi9+iCYy/l37xx9/iIrc9rGoZMiQIcJ+ODrWiBEjhGVqTn4QGBYWJguDRPV4oaCrC5S8QSjHx8QiHRbriKxdu3aaH76L2rvqg1DOVXL6tTt16pRw4SMLxvgzHhUVpd/O/tdTbAjl+KG+0r2BRaQ8x+mqyFYLIJ7TuXPnjvAec+XKFcqdOzeZTCYtXcVKnXXr1gnnn3bt2kXvvfderIzJdqdGC058XSinxMeW45QpUxRF47Z1fXXbCKFc+lSpacNXg4VI3khC9CAplfXjx4+F5VqdLFbVmg7bm9Iiaz0+rfWMFMp9Xr8vNanSTjgUDgCglKpc2ECDU+lzyf727dtr6MGYKhw5b98+cQraWrVq0bZt24zZMXo1jACEcg7QZsucjAb1KGRdS/pe9svKq7T/8D0rf6JEfvRp85xUsog4Eg6Ecla4vGoDQjmvOh0YDAiAQDwgoCQE8/aIcjxJzF+GWaQjsrFjxypOTIvqK/mU+NjWjy9CObVUDGPGjKGhQ4faonG4PWvWLOrevbuw3syZM+mrr74SljnjhFDOGVra6/Kq3S1btlDNmjWFjYx6mAGhnBA3nHGMAIRyceyE4nBAAARAAARAAAQ8ToCFHyyi4d8ttsa/QWfPnm3r1mW7YcOGxBFvRMaRpkNCQujNmzeiYkVfy5YtaenSpcJyd+cjvEUoN23aNOrVq5fiMRYoUMAQUZRwh/9zQiinRuddmTmiy6tXr945dXjHEeM4g4O/v79dbzwXUadOHTu/Hg5PC+V4joOjyYkWIPI97P333ydOJWe0LV68mD755BPhburWrUt//fWXsMwbnBxtk6PH2VpkZCSllQRAnLEits1owQmEcrF9hvXbvxFCuYH1/o+alhGn5Tx6/YpiSlZnjgpCube0jBLKBaVKRz/0+J1SJ08jPC1NmjShVatWCctcdRp933J1XEr3fP4ewtfh8+fPXe0a7WKJAIRyGsD36VKAcoeksKrJixjOX35C/16MoMjIKLm8YJ5U0mqSRFb1LDcglLOk4fn3ndvkppzZrM+jeRSpUiSiBBZzFy9fRtOLl/YTB7fvPqcZ88+bm+EVBEAABEDARQJKQjA9hXI8sfXs2TNdv6CqTaAyikqVKimuKnEGlRIf2z7cnZi27c9bt3PkyEFXr14VDo9X6vOEv9aVY9wJXxvXrl2jZMmSCfts0aIF/fbbb8IyZ5wQyjlDS3tdXsXZp08fYQOOZsDXQ0REhLDcHSeEcu7QQ1tfIQChnK+cKYwTBEAABEAABEDAmwncu3ePONqarS1atIg+/fRTW7cu25wOigVWLM4QGQvp1q9fLypS9HFkDKWUdK4uWjPvzFuEcrly5aLz588LRVE8Vo6SwtFS3LFUqVJJz5AiNQvuIJTTRrtZs2Y0ffp04muJP1t6pMllgStHqWeBlsg4naZIGCWq66zPk0K5/Pnz08GDB4mvTZHx4lFeROoJ46h1nDFAZJwJokSJEobM8Yj256yPs1VcvHhRKIw2Qjzi7Pi4vtGCEwjlXDkr3teG05eWHzGAnjyP1G1w1QsUpm+btVXs77utm6jH+/UVy7UWQCj3lhQL5YJTpaf7EfekSJzRWvGp1ksgCScmdpxLxXOJM++8fv1a/r6r9zy80fct1YNWKTx06BCVKVPGrgYvVPn444/t/HB4PwEI5TSco0wZklL/bgUpIMBCSeWgHd+Dwu88p8wZ3+XdhlDOATSDi3t/XoDy5BQL5bTuOjLyDfUffVRrddQDARAAARBQIKAkBNNTKDdhwgTq2LGjnNKSV2zzRLU71rhxY/r999+FKy253/v371P69Ol1mZhT4mM7/vgilOPj5onK+vXFP545bSmnL9VqEydOJJ7cFBlfg1mzZpVFlqJyZ3wQytnTypQpE/HkC08kOmu8ynnQoEE0btw4xaZGprGFUE4ROwp8iIBfokSU8T3xal4+jIyVy1HCZO9+w7LvwcmzFHnrDr+1s4hL1yji6nU7PxwgAAIgAAIgAAIgEJ8JKAnMOKJboUKFZGGWEXzUIqcfP36cKleurPm3Lj/wW716tXAOhFMh5smTh1jE4qp5i1COx88L5Vh0JTKeI2DR1IEDB0TFDn0s8mGOU6dOJT4/WgxCOS2USD5n5kWOR44cob59+xLPw7hjnIaU05EqGZ9P/iwZYZ4SyrE4jq9njpYoMiMFvaL9sU/pnsllnN6UBcbOLJDldrZWpEgRGjhwoLy4smrVqrbFLm9v2rRJKKzkaH2NGjVyuV+9GhotOIFQTq8zFbv98Jz76rV/0MYTR+nX/Tvp6r27bg0oW9pg+n8dv6RUSaznlyw7bTVnOi3r2tvS5dJ7COXeYmOh3PgOP1LGwCz0++5F9PeRtfTytetRLXkevm2tL+iTmp8rnhe+d3PKUb3N6PuWK+PlfzPPnj0rbFqvXj36888/hWVwejcBCOU0np8iBdJQx9a5KLGUXtWRsZjql5VXKI8Uha5W1Ywx1SGUi0ERK28glIsV7NgpCIAACAgJKAnB9BbK8QQIG4c95skzFrpxmgRnQt/zJBivlG7QoIHwWMzOn3/+mT777DPzpluvSnxsO41PQrly5cqpTkzz+e3QoYPqpH8iSSDCUQG//PJLW5Qx25MnT6b+/fvHbLvzBkI5e3r8GVmwYIH8OWSBIwsgtaz65qiCP/30k+qP79OnT1Px4sWdTidkP0qxB0I5MRd4fYtAwuTJqNRQcURGV47k5pYd9N/WXa40RRsQAAEQAAEQAAEQiLMEOOIUp2cS2a5du2TRx5UrV0TFbvn49xCLbUSpFLljjijHEdQ5upmasaCO0x3yIieR6fFg1JuEcjzvw9G1eM5AZE+fPqV27drJgjdRucjHEf66du1K48ePp6RJk8rpXWfMmCGqaueDUM4OidDB4kazUM5cgQVz33//vZwy2NF1bm7Dr9mzZ5cj03Xq1EkYHYzr3L59m3jxHwtFjTBPCOX43sDCTaXIN4cPH6YqVarQixcvjDhExT6rVatG//zzj+K9i1PhfvHFF059BnlnnD6XU8jyPOCHH34o98/HKIoIpDg4BwVKc0Wcio+vF858EJtmtOAEQrnYPLv67ZuFcjxHy8b3uP2XL9Dm08dp57kz9OSF9nSSQSlSUocqNahRqfKUSPr8Kdl/jx7Qx7MmUeiwiUpVNPshlHuLyiyUK5Wnoux4/OwhbTu2gXad2kJnrh936t+usvmq0Ke1v6S8WQqpngejoo8afd9SPSiFQn4+yN9dbY2/z+fOndspvrZ9YDv2CEAo5wR7jizXtkkI5cgm/oEYFRVNh088pLV/3aRHT17T+5JIrlH9rDF72HPoHi1ZdTVm29U3yZMlpPFfF5e+5PHpIylkt4n6jTpKr187F0qzfctcVKb4u1Dsi367QgeO3rcaVr5cKemrzvljfOcuPaGZgtSjYwYWo8A0iWPqjZt5hsLC1X9wx1T20JtOrXNTyaKBbu3t4aNXNGTiCbf6QGMQAAEQAAEiJSGYUUI5S+acqnPHjh3EIjNe/cxfZs2vXC8kJET+4/D5nGKEQ+UrTTKb+71+/bo8yXL37l2zy61XJT62ncYnoRwfu9IqTTOXU6dOyZNfHAabxZFm48nuokWLyqu3OT2ukvEkasGCBYnPpx4GoZw9RRbKLVy4MKaAP39Lly6VIyqYP4v8UIDT4mbOnJlYINmyZUuqU6eO4kML7owncbjO33//HdO33m+UJj/5npI6dWq9d6faX/PmzWn58uWqdbiQV7pxxAoYCJgJQChnJoFXEAABEAABEAABEDCOAAvNdu/erbgDXrw3d+5cYlHP1atX6caNG4oL+m7evKnYj6iA++V0oUrGaUY5Ejf/drJdtJQuXTrih54cyZsFJiLjNFssMjlxwr05cm8SyvFxsrDRUUpNZsbZC3bu3Ck9i3ktwkM8l8Rpbrm/bNmyxdTp1asXQShHcoQtjrSlh4mEcuZ+eX6R55B4/o/nZs6dO2cukl95ni9jxozy+Wrbtq288DJx4nfPt6wq/2+D5zN4kaxR5gmhHM+J7dmzR/EQeA5UrzkxPh6OoqnVxo4dS19//bVqdV4APWnSJHmugwWsIuOIeTyXxJ9Dnk/i7B+WprdQju+VPJ9l+Xk3769bt270448/mjdj5dVowYlIKDepQ20qlD1Y9XgTJ/SX6qQT1rkf8Zxu3H0sLGPnq3tv5205Yh/PIbpjSnxs+5wyZYqiAN62ri9uWwrlLMcfJX2Gj9+8JkWYu0NhDx/Qf48eyq9hktAt8tVLypAqDWVOEyj/5c+YmT4qUZaSSPPwavZa6rProrl0QuoXQjk1Us6V2QrlLFvff3KXTl87Srce3KRbD8Mo/AH/3aS7j8MpccIAypg2qxyJLkNgZqpS+H0qElLSsrnw/YkroVQ6X0XNaeWFnSg4lT6X7G/fvr1CK+Pc/J2Bnx+GSM8NbY2/M3P2IphvEkggDduY5Q++yUPTqLNlTkb5cqektJIw7O79V5QsqT/de/CSTp97TM8iozT1gUogAAIgAAIgEJ8JKAnBPCGU05s7r7LkSXCe4NbLlPjY9h/fhHIc4nr//v0ORUk8EXfmzBlZDMmRyAoXLkyOJjyZLT9MmD9/vi1ml7chlLNHZyuUs6/hmqdPnz5ytEDXWmtrBaGcNk6o5d0EIJTz7vOD0YEACIAACIAACMQdAseOHZMjXrtzRFFRUaoLhkR98yIeXjDDkYzUjMUFLObjKFkc8SxXrlz03nvvUcKECdWaEYtZhgwZolpHS6G3CeX4IejatWvlqFOOxs+LpVhsFB4eTjyPlTJlSgoODpYX6HFKWpFBKPeWCgtbPCGUsz0HLE69d++e/MfXO0eQS5IkiW01xe05c+bIEQIVKwgK+LPkTEYLPz9xNiteGKg1it2SJUuIhX9KxilHWejpCeN7EX9WtBoLzjhaJd+HHBnzYIEx3+suXrwo37f4M5gvXz55DlCJJfert1CO+xw2bBiNHDmS31rZ3r175TljK6eHN4wWnIiEcquHtqCyeTMbfqQsTGaxuTumxMe2z/gqlLPloMf2+A2radWRA3JXEMrpQfRtH2pCOf328ranu49v0ZfftaQVQ3bo3bXcn9LnMraEckr/dnLk0KxZs5JewTMMgYlOHRJgoRz+wADXAK4BXAO4BnAN4Brw2DUgCcGkOQ17e/jwoW5jkFb52u/AAI80AaXbmM3fyVq3bq1ppNJkkO77No/BW19r165tklZua+LjTKVly5bpzlJK3aJpCN27d9d93956/iShnCYmzlQaNWqUR/hJQjnhsB4/fuyR/VueU60cJcGox8dmOU68977f2f5JAkxlxw42lZswRJe/9OVL4RrD90dcA7gGcA3gGsA1gGsA14DgGpAiN7n925V/+7rynVqKjm969uyZ8PeLO86tW7eapJSiLo3J9jgkoZxwKFKEeF36t92flm0pEpVJSjkrHJe7TilSn+bjkkSMwt21atVKcx+WxyulnBT25+r1Zdl3hgwZhH0rOXk+0LK9O++liHJKu9HVf+DAAZO0+NLpcUtCOV3HoaWzxYsXq45TetivpRtd6vDnydnzGxQUZJLSU+uyf6VOQkNDnR6Xo+OQMiIo3u8lEbLu+3M0HstySVgiRCFle9BlXJJQzmT7d/B8mHCfejul6E5uH4OUvlnTsCZPnuz2vizPi7e9lyLKaeLgbqXVhw+YyowaGPOnBwf+/Gk1SdQUZ8+jJJQzHb6wVysKl+u9fP3CJInkTLw/Pc6fqA+j71uifar5JLG8kJckTjeMgdp4UKbrfLuuneGCEPwoxgWLawzXAK4BXAO4BnANWF8DcUEoJ6XpNBklcJo9e7bwy7etU0r5EC+/e3Xs2FFxAsqWkZZtnhyS0rPqyjJ58uQmaVWRw91LqW5MpUrFH6GJVoGXQ3BSBZ7UHzFihK7nTe1e7U1CuXnz5mlBZOJ6aseEMut/m8ADPHAN4BrANYBrANcArgFcA7gG9LwG+PePO4I1d4RMLIiRojlp+t2gpdLGjRtNUgQu3X5feKNQjs89i5t++OEHLUicqgOh3FtcviaUkyJDmqSUmi5d9xDKOS+U488gi3F/+eUXpz5fzlQ2QijH4165cqVwGFKkOZeuH73+LTJacGIrkuNtXxLKSenNhefN1ilFw4zV86jX9aDUjyeEcquPHDRVHPt1jEiOBXNK43HGr1U0zQEH+L7sTN++VNcTQrkHEfdMXy/sJovk4otQjp/xPHjwwPaWIG9LEVDj7PXkS9e+m2PFj183AeJDAHEgrgFcA7gGcA3gGnDyGvCEUI4jvUlh+IVfYt11SqHzTfnz5zfsvHMUKEfGIqzYXpUYm9+hSpcubTp58qQjTKrl/MBiwIABhpzHOnXqqO7bXMgTabHJ0dP7rlevnvnQ3XrlKAMlSpTwKDtvEsrx5I4jk1K8mKR0Mh5l5OnrCfvDb3lcA7gGcA3gGsA1gGsA1wCuAW+/BnLnzm3asmWLo6/vwnJ3hHLMhX8z7d+/X9i3VueLFy9MHLHflYhaaufGW4Vy5jE3b97c9O+//2rFpFiPf5f9+uuvJiklpObfZogop+2+JqX2NHXq1Mm0YcMGE1+nehrPufHCPHcWVUIo55pQzvwZbNeunenSpUu6ndaIiAjT8uXLTQ0bNtT8WTSPRcsrz1PyYlhbY4GFK9H1tOxTSx0I5ZTvJ3xf1mJGiSu1nD9P1eHvCxdvhwuvYS2M1OrcevzI1HPJAiuBnDmqnB7H9/3336vtPqbMiKxAeoxfrz5YuLZk21xTROTjmGPW883243+amo15L0YkF1+Ecv379xdi5Oinep079KN8n/YAm1jdOS4iJ4UFHrggcE5wTnAN4BrANYBrwPBrwBNCOfO/mfyjt1u3bqbff//ddP/+feEXWy1O8+Rm5cqVDeWTMWNGLcMxcchn8zHG11deZTpw4EDTqVOnNDEzV2KB3KxZs0zM2ih248ePN+9O8fXNmzemIkWKGDYGo47NnX6TJUtmaty4sYmjITr7eeSHRPyAqUOHDiZ/f3+Pc/MWoRyvZtdifI27c67QFr+TcQ3gGsA1gGsA1wCuAVwDuAZwDeh3DWTJksXUvn1709KlS00HDx408eIXFlDw70Il40g37p6DBAkSmNq0aWM6ffq00m6Efo6iz2M1aoEeC5xEtn79ereP2V1m5vb8u5OjAvJCLbXzJDqOsLAw09ChQ02cmtTcn9bXo0ePiro01axZ0+m+eJ9ly5YV9qfH9RUYGKgpmr55ABUrVnTpGByxS5kypalFixamZcuWuRVJkYVZX3/9tSlTpkxuj5M/e3fv3jUfukdep0yZojpuXvTrCXv06JEu4loWKnbp0sV05MgRlwQ89+7dMy1YsEAWx+kZEVPpeuS5LpENGzZM9bwo9aeHPzaEcttPXBVh0NXH4li+/7jDiK8tLcaLft3Zj6+0ZfFanSmjTd+sWmJaI0V/C3sojqKlhRnXOXTlomnQ74tNFcZYR5Ezi+T0iih39uxZh0PioAR+fn5x+jyycI3/Pvi6hJwadf6maabQ83tNL149d8hHqULE8yemVXsWmzpO/dhKIGfel1HXttH3La3j5mhyd+7cscPDougyZcrE6etJKyNfr5fgfwcgvcBAAARAAARAAARAwDMEJKEcrV692m5n0sQjHThwwM5vdkiiKNq9e7d50+lX6QcRSSukqFChQiRNVtr9SROZJE0akTShQw8fPiRpQoWkyRh5TIcPHyZpotjpfTrboGXLliRNRqs2k0R7lDdvXpIi5qnWi0+FfE6lMPgkrdYnaUKTJBEc8fnkbT6P/CetBidJaEV79uwhZmikSav2qXz58qq74PPcunVr1TpxuVBaXU1SOiAqV64cSQ+NYv4kQRxJ0Qrkc3Tr1i0KDw+n48eP0x9//EGSuM5QJGPHjqXq1asL95E2bVoqUKCAXZmUzoiklex2fqMc0qpqkiZfVbt//vy5fO0zOxgIgAAIgAAIgAAIgAAIgAAIMIGcOXNSgwYN5N9g6dOnJ/6TxHqULl06kqItkfQwkK5fv06bN2+mf/75h/h3BewtgaCgIJKEaiSl2ZLnHIKDg2VuV65ckecfLl++TOa/CxcukBRphKKiooAvFghIiyqpVKlSJC0yi5lnyJo1q/xeEtTJ1zyfz8ePH8t/fL54zk+KGkXnz58n6Yl4LIwau3REgO9TtWrVoipVqsR8BvlzyJ9BnlPiOVL+43sYv/LnURK5Es81e8r4OuNrKGnSpFa75Hlmvv/yq6dNEpzQp59+ardbScRJfO2LTBKByPN1ojJbn5Rq1dZl+HbYL3102YckrCVJYKval5RVhqRF86p14kqhJFyzO5QsgWmpYKYsFJQ8JaVNkYLSSq9B0itvs8DklfT5SpooET158ZwipL9Ld27TqbDrdPq/m/Qo8pldf7aO0GETbV1ObfMzACkKq8M2fJ5/++03h/V8uYIkXhMOP6F/IiqYvRhlCsxKaVIGUWCKtNJfsPTH74Po9qMwypw2u3T+ntCz5xH0IOIunbt5mv69cZKu3DpPb6KV76Gbx58Q7lOLk5/xSYJyYVV+7sX3fFvj+5m06MTWbdg2P4uUoirb9b948WKSIhTa+eHwPQJ8BeJbn++dN4wYBEAABEAABHyagJJQztFBffzxx7R27VpH1Xy6fO7cudS5c2fVY5AiRVHPnj1V66Aw9ghIKRXkiVdp9bniIHiijsV9PIEG8x4CUuRJatKkiVMD8rRQTmmS03LQ0ipy6tevn6UL70EABEAABEAABEAABEAABEAABEAABEAABAwkMGrUKJKiSdrtYfTo0SRFlrPzG+3QModkOwYWi/Jicy3my0K527dvy4JxteNkcea2bdvUqsSZMpFQzuiDc1coxwvQpfTmqsPkxdclS5aM8yJoJaGcKhw3C90RyrEgV0kopzQsTwrlUkiCUBZisyDb0ngRiZTBiqSovJZuvPdRAhDK+eiJw7BBAARAAARAwJcJQCinfPZ4NV+ePHkUK3BUO46SxpG2YN5JgFfoS+liVAe3cOFCklKIqtZBoecJ+IJQ7tq1a5Q9e3ZFOE+fPpVXKnMURRgIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgIBnCLC4gud2OdOFpfEiS44qx1E8PWkQyolpFy5cmE6dOiUu/J+XI6tyJNH4Yr4olJs3bx5JqdxVT1F8CLzAACCUU70MnC4cNGgQjR8/3q5dbIme7QYChy4EIJTTBSM6AQEQAAEQAAEQcIYAhHJiWhyi31E61W+//ZYGDBgg7gBeryAwefJk6tu3r+JYOLUorzy6evWqYh0UxA4BbxfK5cqViy5duqQKh9PHDhkyRLUOCkEABEAABEAABEAABEAABEAABEAABEAABPQn0LFjR5o/f75dx+PGjaNvvvnGzm+kA0I5Md3u3bsTZ2xRM065yqlX44v5olDu4sWLckABpXN08OBBKl++vFJxnPJDKKff6eQ07RxNjtO0W1p4eDhxWthnzxynFbZsh/feSwBCOe89NxgZCIAACIAACMRZAh999BH98ccfTh9fXF8B1LZtW1q0aJEil4iICGKhDCJFKSLyioLDhw9TqVKlFMfyww8/0BdffKFYjoLYIzBnzhz6/PPPnRqAJ1OvKk22mgf86NEjeYUyv8JAAARAAARAAARAAARAAARAAARAAARAAAQ8S4DTlh49epSKFStmtWPOAJAhQwbibCGesgULFlD79u2d2p1TqVfbTCEpf6JT/btVWUoLG7ZYeXGy1r5XrlxJjRs3Vqy+adMmql+/vmJ5XCwoPXKA06kw3eHA19nh4ZNc7iJbtmx0/fp11fYffPABbd68WbVOXCn0NaHc3bt37dKaOjoXnkq92q5dO/r555/thsPZgThLECzuEIBQLu6cSxwJCIAACIAACPgMgQTSD+iSJUtS4sSJnRrzuXPn6OHDh0618aXKjiYvENrZ+89mYGCgLGTkSTGRvXjxQk6tGxYWJiqGL5YJJEuWzG4i09GQoqKiKDQ01FE1XcoXL15Mn3zyiWJfQ4cOpTFjxiiWowAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQMBYAlmyZCEW8tgaL67lTBOeMo6MxGlGnTEWMB04cEBTkyxtJktCOfEcqKYOnK1kipaEcv2cbWVVn59LsEjHNlqUZaUyZcoQn6v4ZKVG9Cel+WwjOERHR9OREd+63LWSmMnc4a5du+i9994zb8b519qDinlas0pbJpxwmStnVuI/Z4w/t44yrTjTn1Jdf39/4nsA3yvMxvfFQ4cOEV+3sLhDAEK5uHMucSQgAAIgAAIgAAI+ToBDOoeEhAiPggWCHE0OkaKEeLzGyVEP16xZozie6dOnU+/evRXLUQACagRYYJk5c2ZhFY40yfcIjjwJAwEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEjCWRuPYkS+Cc0chdWfZveRNF/SwZY+ZzdKF68OB07dkyxGc/rNmrUSLE8rhaUGtaX/BJ67lxGSwuPj4ySIhK6aBzZ67PPPlNsXb16ddqxY4dieVwrqD1QEsp5VrNKWya6LpSLa/xxPL5JAEI53zxvGDUIgAAIgAAIgEAcI5AzZ066fPmy4lENGTKExo4dq1iOAu8gMGPGDOrZs6dwMM+ePZOFTHfu3BGWwwkCagTy589P//77r2KVAQMG0Lffur4SU7FjFIAACIAACIAACIAACIAACIAACIAACIAACICADYHMLcdTgkQBNl7jNk2vX9B/y752awe8gHnq1KnCPjhqFAvpTp48KSyPy86S3/Qi/wDPncs3L1/S0bHTXUZ67do1yp49u7D933//TbVr1xaWxVXn+/2LSkJHlv14xqKjTPT3t/Hvc+IZutiLpwhAKOcp0tgPCIAACIAACIAACKgQ6NChA/3000/CGhxWmiNFPX36VFgOp/cQOHHiBBUtWlQ4oAkTJtDgwYOFZXCCgCMCXbt2pR9++EFY7datW5Q7d26KjIwUlsMJAiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAnoSyNR0BPklTaVnl6p9RT9/QuG/j1Ct46hw7dq11LBhQ2G1ZcuWUatWrYRlcd1ZrP+XlDhlCo8d5quIp3Ti29ku7Y+fk6il4KxQoYLm9MEuDcALG9XsW4QSJvZcSLmol9G0beopLySBIYGAdgIQymlnhZogAAIgAAIgAAIgYBiB77//nrp16ybsv1+/fjRliuuhyIWdwqk7gWTJktHjx48poSBM/ZMnT4ijBj548ED3/aLD+EFg/vz51LFjR/lgn0e/oWvSKtp7b17SIyntxJpNG+noqVOUIKEfmaKiKerFC3r9JIKe371Hz26G06PT5+j1M4jo4seVgqMEARAAARAAARAAARAAARAAARAAARAAAeMJZPhoECVMnd74Hf1vD1GP79DttRPc2t9///1HmTJlsuvjzZs3VLhwYTp37pxdWXxwFO7RiZKmC/LYoT6/e59Oz5rv0v6aN29Oy5cvF7Zdv369ohBS2CCOOKv1KEQBKTyXOvfl0yjaMetMHKGHw4ivBCCUi69nHscNAiAAAiAAAiDgVQSCg4OpSpUqFB0dTRzm3fzH29u3b6fnz5971XgxGDGB8uXLU8aMGeXzZ3kuOeLXkSNHxI3gBQENBNKnT0+VatUkv8J5yS+rNKHnl4CkOwXxfzdv3iSe0FOyaKns4cmzFLZtF7169ESpGvwgAAIgAAIgAAIgAAIgAAIgAAIgAAIgAAIgoIlAcO0vKCBjHk119aj08tYFurdFnG1Ba/+cCYQjklnOvfN7Xvy8Z88erd3EuXr52reiVDnFqUyNONgnV67R+YXLXOra39+f6tevT35+fnbPUo4fP05hYWEu9evLjSp1zk8pgj2XOvfpvZe0d178FJX68nWCsVsTgFDOmge2QAAEQAAEQAAEQAAEQAAEQMDrCKSvUIay1q5GfolcXx0Y/TqKbm7ZQXf2h3rd8WFAIAACIAACIAACIAACIAACIAACIAACIAACvkMgTYVmlDxvRY8N+NmFffRo/wqP7S8+7SjHR3UpXZniHjvku6HH6draPz22v7i+ozKf5KK02T2XOvfBtacUuuRyXMeK44vjBCCUi+MnGIcHAiAAAiAAAiAAAiAAAiDguwQSSKsjc/xfXQouUVS3g7h37CRdW/MnmaSIlTAQAAEQAAEQAAEQAAEQAAEQAAEQAAEQAAEQcJZAsjzlKbBiC2ebuVz/4b7lFHnxgMvt0VCZQFCpYpTz/+opV9C55MqaTXT/yAmde42/3RWql4WylvBc6tybx+7TmU3xL3Jf/L3C4uaRQygXN88rjgoEQAAEQAAEQAAEQAAEQCAOEAhpXF9XkZwZCYvlrq7aaN7EKwiAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAhoJuAXkIIyNh1OCfz8NbdxtaIp+g3d+n0kRb986moXaKdCIGHyZFSs3xfkJ6U1Ndqi37yhE5O/p6hnkUbvKt70n6VYWircIKvHjvf0hpsUduKBx/aHHYGAEQQglDOCKvoEARAAARAAARAAARAAARAAATcJcLrV7PVrudmLcvPrG7ciDasyHpSAAAiAAAiAAAiAAAiAAAiAAAiAAAiAAAioEAis1JqS5S6jUkOfoshLofRw7xJ9OkMvQgI5GzegoBJFhGV6Ou8fO0VXVm3Qs8t431fiZP70XvdCktCRpT/GWvQbE+387gy9inxj7I7QOwgYTABCOYMBo3sQAAEQAAEQAAEQAAEQAAEQcJZA4jSpqEiPzuSXKKGzTTXXj34dRadmzaNXj55oboOKIAACIAACIAACIAACIAACIAACIAACIAACIMAE/JOnpQwfDaQECRMZBsQU9Zpur51Ib54hgpVhkKWOMRdpJF3j+y7SMBtlLhJo+I7+O/WQTq27Yfh+sAMQMJoAhHJGE0b/IAACIAACIAACIAACIAACIPD/2bsX6KjqOw/g38xMkkkCTIYEeaMhCMojGBe3uvjCbhFcQBHFWmoFu2hXV936WOtyCoXT1u6Kp4t7ij1Uu+iy2YpaeVWxIlV5+AAEAoGACDUQQkLeMZlknvv/33lkZu5McifzyEzyvedw5s7//u/rcyHK8J3fL0IBfoszQjBOpwAFKEABClCAAhSgAAUoQAEKUIACFEi4QM4VNyD3mvlxO2/jvrfRWr4rbsfngTsF2N2i0yLV1rJMGZj+4AToDPGrKue0u7Bn3QlYmqypxsPrpYBKgEE5FQkHKEABClCAAhSgAAUoQAEK9J6AIScbRU89LMrl6+N+EU6HA6Wr18Le2hb3c/EEFKAABShAAQpQgAIUoAAFKEABClCAAn1PwPx394oWrNfE/MbavtonWq7+X8yPywOGF7jsztuQf9WU8BN6uKX24BH89e13erg3d9MiMGZaPq74zggtU3s0p/z986jYX9ujfbkTBZJNgEG5ZHsivB4KUIACFKAABShAAQpQoF8L5F1dhII7ZifM4Mymd1H3RWnCzscTUYACFKAABShAAQpQgAIUoAAFKEABCvQhgTQdzNfdE9OwnBKS++R1wOXsQ1DJfytpOh0uvWNWTMNytYeO4OtN28Wj5LOM9++AyXNEC9YpsW/Bev6IaLm6jS1X4/38ePzECTAolzhrnokCFKAABShAAQpQgAIUoEC3ApfOm4Uh06Z2Oy9WEy7uP4yvt2yP1eF4HApQgAIUoAAFKEABClCAAhSgAAUoQIF+KCDbsJqK5yDNkN7ju3fZbWg6uI3tVnsimCaiHy5XT/ZU7SPbsI76zk3QpRtU27QOOG12nHv/I9R8ul/rLpwXpUCaLg2TbhsV07CcDMmVvXNOBB1j83srylvk7hSIiQCDcjFh5EEoQAEKUIACFIhEoLCwEHPnzo1kF2Xuli1bcPr06Yj34w4UoAAFtAjMmTMH48aN0zLVN8dqtWLt2rW+97FYGb/kXgwqGOM71IQJE2DMMvrea1mxtFlw8uRJLVPRfKYCJ/878jYWjz76KPQRtoctLy/H9u3xD+UVFBTg5ptvVt3/66+/jrY2tplVwXCAAhSgAAUoQAEKUIACFKAABShAAQrEQECfMxiDps5CVkEx0nR6zUd0OR2wnDmI5sPb4Wit17wfJ3YKTPrnB1B/tBwXPz8Eeww+/8rIHYSRt9wA85QroYvgM0Cnw4GGI8dRuXMXrI3NnRfItYQJyDas42cMh84g40A9W5x2F07+pYrtVnvGx72SXIBBuSR/QLw8ClCAAhSgQF8UuOOOO/D2229HfGu33347ZFiOCwUoQIF4CLz55ptYsGBBRIdubm6GyWSKaJ/uJk969B+RNSTPN+3e792LvLzO974NXazU1NRg4+sbu5jRuclysQ5l//Vy54DGtfb2dmRmZmqc7Z5WUlKCRYsWRbRPTyYvXLgQMhQXvKxYsQKrVq0KHuZ7ClCAAhSgAAUoQAEKUIACFKAABShAgRgK6IwDkTWmCBn5Y5BuGgZ9jglp6VlI0xvgctjhsllEIK4JtqYLsNZWwFJRCmd7SwyvoP8dquiph2HMHwxZya2+tAzVe/ejtbIqaoj0nGzkTpqAnFHDxWeW+UgfNBAGo1FUDtTBZXfCLj4jtDW3wHKxFq3nqtBYdgK2Vn5RNWr4KA+QZcpA4Y1DMezKXBF01B6YczpcuHC8EV99XA1LkzXKq+DuFEhOAQblkvO58KooQAEKUIACfVqAQbk+/Xh5cxRIWYFkCcoVPf0IMgYO8DnGOyhnbfkGpc//xnc+rSvJHJSTle6OHTuG8ePHB9xOa2urMnb+/PmAcb6hAAUoQAEKUIACFKAABShAAQpQgAIUoEAqC0x85AEMGD0i4BZavj6LGhGYqxcV3lxOZ8A2vukfAhk5BgydYIJpRDYG5BuROTAd6Ua9CK2midCqC7Z2BzpabPimth1N59tQfaIJ1lZ7/8DhXfZbAQbl+u2j541TgAIUoAAFek+AQbnes+eZKUCB8ALJEpQrXvYv0PtVaot3UM7R0YGDv/jP8DBhtiRzUE5esqxct2HDBtXVr1+/HkuWLFGNc4ACFKAABShAAQpQgAIUoAAFKEABClCAAqkqMOGB78E0fmzIy7eJL8pWf/YFLn52ALaW1pBzOEgBClCgvwgwKNdfnjTvkwIUoAAFKJBEAj0Nys2bNw9bt26N+Z0YDAaMHj0aY8aMgVN8q6q+vl75VVtbC5vNFvPz8YAUoEByCiRLUO7q5U9CJ34ueZd4B+Wcdju+WPWC93SaX5M9KKfT6XD06FFceeWVAfckf85PmzYNBw8eDBjnGwpQgAIUoAAFKEABClCAAhSgAAUoQAEKpKpA4ffuRF7RxC4v3+VwKNXlZFvWbyrOdTmXGylAAQr0VQEG5frqk+V9UYACFKAABZJYoKug3AsvhA5ryHZ5zz33HGQwI5qloKAAd999txKckOuXXXYZRo0aBdmmL3ixWq3Yv38/du/ejW3btmHXrl3BU/g+BQQWLlyI4cOHh73S1157DQ0NDWG3c0NsBGQY9c4774zNwUIc5cCBA8qf1RCbNA/94Ac/QFFRUcj5hYWFkD+7gpfm5maYTKbg4ajeX/2zpyFDXt6luLgY2TnZ3rcBr0MvGYoRIwNbKsgJNTU12Pj6xoC54d7I4NgXP3s+3Oaw47/61a8gg8ahlpkzZ2LKlCmqTSUlJUqlN9WGOA3cc889+MMf/qA6+ocffogZM2aoxjlAAQpQgAIUoAAFKEABClCAAhSgAAUoQIFUFBj73TuQf9VkzZfeWlmF6j37UFdaBpfdoXk/TqQABSiQ6gIMyqX6E+T1U4ACFKAABVJQIFxQrrGxEWazOeZ3lJubq4Tj7rvvPlx//fVIS5P/CxT5IqsPySCfDHq4XK7ID8A9Ei4gn/fHH3/c5TO/4oorcOLEiYRfW3874be//W3s2LEjbre9evVqPP3003E7vqxouXnzZtXx4xGU+5uV/9rl71n/iyi+uhjTp0/3H1LWIwnKyZ9nB1b8h+oY0QysW7cOS5cuVR0i0UE5+fO+tLQUkyerPyScP38+Nm3apLpGDlCAAhSgAAUoQAEKUIACFKAABShAAQpQINECaXod9EYjDFlG6OUvuW7M9IyJV8+Yb47cJsay8gcjLUQhAK3Xb21uwYlX/heW6lqtu3BeggRy8jNhabTCaee/RyWInKfpJwIMyvWTB83bpAAFKEABCiSTQKKCcjfddBMeeeQRzJ07F0bxl8pYLe+99x6WLFmCqqqqWB2Sx4mDQEZGBg4dOqRquxh8KgblgkXi855BOe2u01Y9o3lyLIJy8mT7l/+75nNqmZgsQTl5rQsWLIBsqxu8nDp1ChMnTmSL7WAYvqcABShAAQpQgAIUoAAFKEABClCAAhSIWECXblBCbTK8ZhD/HqEXQTYl9OYJtLnX3eO+sFtWJoz5eZq/NBvxRXWzQ+OJU6jY+j7aa+u6mcnNvSFw+c3DUHDdJag+0YSqskbUnmqG08HQXG88C56zbwkwKNe3nifvhgIUoAAFKJASAokKym3duhVz5syJi0ltbS1mzZoF2e6RS3IKLF++HCtXruz24hiU65YoJhMYlNPOyKCcdistM2VVOVk18vLLL1dNl62Z33jjDdU4ByhAAQpQgAIUoAAFKEABClCAAhSgAAX6j4AuPR0QyQlvgM0/4Oar7Oat8qa8ihCcCMNlmE1IH5CTclDttfWo2PZnNJafSrlr708XPKLIjKnzL/Pdsq3dgZryRpwXobm60y1wORma8+FwhQIRCDAoFwEWp1KAAhSgAAUoEBuBvhCUkxIyLHfDDTegvLw8NjA8SswEZPhNVpPLzMzs9pgMynVLFJMJDMppZ2RQTruV1pk//elPsWrVKtX0nTt3Qv7e5EIBClCAAhSgAAUoQAEKUIACFKAABSiQ2gKygps36Ka0KRWfDfsH3rwV3ZR2pp7QW2ZerhJ4S7Y7t7e3w2HpgEO82i1iXb5vF+/Funxvl+tBY9kjhmLsXXO7vBV5jMqdu1C9+3MRsnJ2OZcbe1/APDoH1z4wPuSF2Cx2XDjeqFSaqz8jQnPMzIV04iAFQgkwKBdKhWMUoAAFKEABCsRVIJmCcna7HWVlZaisrFSCb4MGDcKECRNQWFgI2bqzu+XMmTOYNGkSLBZLd1O5PUECsnrURx99pIQYtZySQTktStHPiXdQ7pe//CWWLVsW/YWGOcK8efOwefNm1dbm5maYTCbVeDQDDMpFoxd637Fjx+Krr74KuVH+zD958mTIbRykAAUoQAEKUIACFKAABShAAQpQgAIUSICA+EzXINqQyqCbL+zmaVnqreimBN081dy8czIHm6HP7P5z/ATcge8UMoCmhNqUkJsn0OYXblOCb0r4rUME3joDcLLKm9Nm8x0n0pXcK8Zh/OLvhtzNJVJUtfsP4+z2nbC3toWcw8HkE8jMMeCWp6Z0e2HWVhsuHPOE5iq+ARia69aME/q3AINy/fv58+4pQAEKUIACvSLQ20G56upqlJSUYMeOHdi1axdaWlpUDgaDAbNnz8aaNWtQUFCg2u4/sGLFipCVivzncD1xAkuXLsW6des0n5BBOc1UUU3sKij3wx/+MKpjyw963nzzzZB/lqM6sN/ODMr5YWhYlX8G5Z/F4EX+7F20aFHwcELe7969G9OnT1ed69e//jWeeOIJ1TgHKEABClCAAhSgAAUoQAEKUIACFKAABbQJpOl0kFXcZDtSpZqbDLR5K7qJAJxvXGz3r+hmFBXdlLan2k6TkFlOh8NdrU1WdFMCbu4wm6+ym1/ozb/qW3tdPVx2R0KuMdRJBlw6ChP/abFqU8tfz6Ji65/RWlml2saB5BeY+WwR9Bl6zRfa0WJTqsxVlTWg8Vyr5v04kQL9SYBBuf70tHmvFKAABShAgSQR6K2gnGyx99vf/habNm2CTeM3s7KysiBb9j377LNh9dra2jB69GjU19eHncMNiREYPnw4jh07htzcXM0nZFBOM1VUE8MF5WRVx/T09KiOnYidGZSLTDkZg3IPPfSQ8t+A4DuRP7tHjhyJdvENXi7JJyC/LS6/HZ5hNsFozlVeXU4X5Les5QfQHbV1sDY2i/YS/Kps8j09XhEFKEABClCAAhSgAAUoQAEKpIpAmvjieKiKbt52pfqAsJuo/OYJxRnzzEjTaw/xJMJDVmWTFd287Ut9ATdPu9KAim5+ld06xGcNqdyONGtoPqb8+Ec+YmtTM86+uxN1h476xriSGgIGox7Z5kzkDM7AxFmjkDEg8s/PKw/X49i7Z2HvYIvd1HjqvMpECjAol0htnosCFKAABShAAUUg0UG5Tz75BM8884xSPa6nj2DDhg1dVkG6//778dprr/X08NwvRgIbN27E3XffHXC0mpoa7NmzB/Pnzw8Y975hUM4rEd9XBuW0+7L1qnarSGaazWZcuHAhZFvtxYsX49VXX43kcJwbRwFDTjbypk5C/tVTkDNqRLdnkh+AV3+yH+d37lY+CO92B06gAAUoQAEKUIACFKAABShAAQr0MQFZlU0Jr3nal/oCbrKCm7elqQy3eSq9ueeKL6fJoJtoe5pMi8NqFZXcvEG3oGpuslWprOgmq73J1qae8Jsck1+q689L+qCBKP63x0X7VjuqPv4EVR/ujaqVa3+2jPu9iz9yxoHpyDJnIEcE4mQoLmuwCMbJ93my6mLPw6eyDevRbWdRXd4U99vgCSiQqgIMyqXqk+N1U4ACFKAABVJYIFFBORnMGThwoFJBLlquvLw8HD9+HEOGDAl5qD/+8Y9YsGBByG0cTIzA3LlzsWXLFtXJlixZghtvvBHyNdTCoFwoldiPMSin3ZRBOe1Wkc6UP6tDhWY//fRTXHfddZEejvNjLJAzeiRG3DIduRPGQbZsiXSxt7bh3Psf4eLnX4hvgLPCXKR+nE8BClCAAhSgAAUoQAEKUIACvSegU9qUBrYo1RvFexlsk7/kuhJ6c6+7x4yQFd2SbVEqufkCbCLwplRtkwE3T/hNBtw8ldxkwE1Wf+toaIS9zZJst5JS1yPDkmMXzkPFn3aI6vsMSfX2w9MZ0pBlykC2CMBliwCcDMP51sWYTh/5Z1/d3VP18UYc/dNZWFvt3U3ldgr0awEG5fr14+fNU4ACFKAABXpHIFFBuVjf3ZNPPonVq1eHPOzp06dRWFgYchsH4y8gA5FlZWVKC1z/s8lqgtOnT8crr7zCoJw/TC+sMyinHZ1BOe1Wkc687777Qlb/lG07L7nkEtTW1kZ6SM6PocCoW2dgxIzpUR+xoewEvvyfN6I+Dg9AAQpQgAIUoAAFKEABClCAAhTQJCAqshlEkE0vgmyyRal8dVd081uX1dzkHCX0JteNyDCblHmazpGgSfIzEm94zd261D/g5lflTQTcfIE4sS6DbjLwxoUC/UnAkKlTwm+yCpwMw2XldgbiZEgumsVhdcDSZIXT4cKgYdldHsrW7lDarJ4vbehyHjdSgAJuAQbl+DuBAhSgAAUoQIGEC6RqUO6WW27BBx98ENKrra0NOTk5IbdxMP4Ca9aswWOPPRZwIqfTiWnTpuHgwYP4/e9/z6BcgE7i3zAop92cQTntVpHOHDduHL788suQu8mqoLLiHJfeE4hVUE7ewddb30P1nn29dzM8MwUoQAEKUIACFKAABShAAQqkjICsau4Nufm3LJWht+AqbvK9rO4mA2+Zg3Ohz4guDBNrJJfD4Q6weVqTyvCarN7mq+AmW5d6K7r5tS/tqGtgm85YPwweL/UFRJomc0C6CMPFvkWqxLFZ7Gir70BrgxUW8drWINfFa70VHS02xS+/cCCu+f64sJa1XzXjyJYKtDe754edyA0UoIBPgEE5HwVXKEABClCAAhRIlECqBuWGDRuGqqqqsEwDBgxAa2tr2O3cEB+Bb33rW9i7dy90QW36XnrpJTz88MPKSRmUi499JEdlUE67FoNy2q16MlNWjZPttIOXF198EY8//njwMN8nUCBUUK6joQmN5V/CUl0Dy4WLyi+nww5jfh4GFozByL+/EYbsLNVVyn8YOLZ2PVorw/93W7UTByhAAQpQgAIUoAAFKEABClAgJQXSDIaAim7esJt/BTcZbOt87w7AZQ42Q5duSKp7dtrsSpBNCbgFBdq8oTcZdJNV35TwW4c7CNdR3wCX3ZFU98KLoUAqCHhbpGaJ1qg5skWq0ipVtkl1r0fTItXldKFdBN5kGE6G4NpkIE6G4UQQrrVW/Dm2Obslki1bb3psomqerDh3Ysd5fL2PHTJUOBygQDcCDMp1A8TNFKAABShAAQrEXiBVg3JSokN88JAR5luCsqKcrCzHJXECBvEh2IEDB1BUVBRwUhmEGT9+PBoa3KXGGZQL4OmVNwzKaWdnUE67VU9mvvPOO5g9e7Zq18OHD+Oqq65SjXMgcQL+QTlL9UWc/3Av6g+XwSUqhIZbDDnZGLtwHnInqL9Z23zqryh/eUO4XTlOAQpQgAIUoAAFKEABClCAAkkioEtP97QkFQE2JdAmW5h6Krd525h6W5rKim6eSm+yoluaXp8kd+G+DKfV5qneFtiaNKCim6dtqd1b9U28KkG3Lv7+m1Q3yYuhQIoJeFukytCZ8kuG4DzrWaZ0QLRP7unibZEqQ3D+gTj3ulV8ruXq6aGV/dJ0abh12VTIV+/ScPYblL5doYTvvGN8pQAFtAswKKfdijMpQAEKUIACFIiRQKoG5bKzs8NWjKuvrw9ZoShGZDxMGIGf/OQneO6551Rbly5dipdfftk3zqCcj6LXVhiU007PoJx2q57MXL58OVauXKna1eVyKT/HvQFb1QQOxF1gcNGVGHb9taj6yx40iCpyEM9EyyIryk154kdIHxDYAl1+0/7Az57XcgjOoQAFKEABClCAAhSgAAUoQIEoBHSZskKbux2p91W2MjV4wm3u9c6Am9K6VGzPzDOLfEpn+COKS4jZrt5KbvJVqdqmVG9zrzs81dvsnopucrt3TLYu5UIBCvSSgPgxorRIlRXhZABOqQon2qXK1zwj0o3RBWq9LVKVMJynMpw3FJeIlqc3i4pysuKd0+HEl3+5gDN7q7V+bNZLD4SnpUByCzAol9zPh1dHAQpQgAIU6JMCqRqUmzx5Mo4cORLymRw6dAjFxcUht3EwPgLjxo1DaWkpsrICW+59/vnnuPbaa8VfFDsDFgzKxecZRHJUBuW0azEop92qJzNvvfVWbN++PeSut99+O7Zs2RJyGweTW+CSvy3GZXf+g+oiDz//G/AfK1QsHKAABShAAQpQgAIUoAAFKBAg4G5VKoJuSrW2wICbu2Wp35in0ptBBt1ERbdkWuSngUp4TQbYRNDNF2hTWpiKsJvyKre5f3krulkbm2FvsyTTrfBaKECBIIFQLVJ97VLzMhFti9QO2SJVaYsa2CK1rU5Uh7SG73YQdJlxeXvN9wuRkWMQVeS+RktNe1zOwYNSoD8JMCjXn54275UCFKAABSiQJAKpGpRbtmwZfv7zn4dUfPXVV7F48eKQ2zgYH4EdO3ZAhq/8F6doTyBDcvv27fMfBoNyARy98iZcUE5ezIwZMzBz5kxMnToVQ4cOxZAhQ5RvE9fV1UG20a2pqcFnn32GDz74AGVlZb1y/fPmzcPmzZtV525ubobJZFKNRzPAoFw0et3vazabIX9HOa8AAAHeSURBVH9vhfrG+gsvvICnnnqq+4NwRtIJZA0dgik/fkh1XadK3kJ96XHVOAcoQAEKUIACFKAABShAAQr0FYE0nc4TcJNV3TztSmWYTWlh6g6/KePKmGeOqPSWIT7PkAG5ZFpc4rM9pUWpp1Kbf0U3ZVxUdFPGlCCcCLt5AnHtDU1wim1cKECB1BZQWqQGt0dVqsNlIjYtUm2wKGE40Sa1sV20ShWvskKceI22RWo85c2X5qDxbFtSX2M875/HpkCsBRiUi7Uoj0cBClCAAhSgQLcCqRqUk9XkZFW5UMttt92Gd999N9QmjsVB4P7778f69etVR/7d736HBx98UDXOoJyKJOEDXQXlIrmYyspKrFmzBmvXrg3bCjmS42mdy6CcVin3vHXr1kG2QA5eSkpKsGjRouDhhL8/d+4cRo4cqTrvW2+9hbvuuks1zoHkF0jT63HNL55VXWjFn3bgwq5PVeMcoAAFKEABClCAAhSgAAUokCwCaQZ9YMBNhN1ky9KA0Jsy5g6/+dqWmk3QZ2Qky20o1+G0O9zV2kSAze7fulRpVeqt6OZ+9YXgxLaOehF0s9mS6l54MRSgQBwEglukKm1S3e1Sc0RVuPQsQ1Qn7e0WqVFdPHemAAUSJvD/AAAA///lj77ZAABAAElEQVTs3Ql4ZGd9J+pPJam0uXebNYBNWIxZMiasgcsaMiwTHCZMABMghkkCvizXYQuBcIcQJ0CAS4YtJAwQcwPEMGwz4ZIECEuYsDosBmOzb8Fju7vl7tZaUul+3zmnpJJUpZaqSlJV6T3Po6dOnVOn6pz3CHfT+un/GwghLMUvCwECBAgQIEBgxwR+7dd+LXzgAx9Y93mTk5Ph0KFD67Z3w4bHPOYx4f3vf3/DU7nhhhvCzW9+87CwsNBwv42dFTjrrLPCVVddFY4cObLqjY8dOxbucIc7hKNHj67anp687W1vCxdddNG67WnDueeeG66++uqG+2zsnMBDH/rQ8LGPfaxjb3j99deHF7/4xeGv/uqvOvaeG73Rox/96PChD31o3UtOnDgRDhw4sG57Oxvu8Ucv3PTh59/9/HC/+91v3euvu+66cPnfXr5ue7MNX3rpK5vtamn7X/7lX4bf/u3fXnfsu971rvDEJz5x3fad3vDVr3413O1ud1v3sZ/+9KfDAx/4wHXbbeh+gdLQULjHH//+uhP93uUfDjdc8bV1220gQIAAAQIECBAgQIBApwRKw8NhcGw0DI6OhKHR4jE9r23LHtP2tC2+ptg+cvBgKA0Pdeo0OvI+1UolLMzMhsX4tTA3lz1m67PFenqcjfvSa5Yf58Lc8cmwtLDYkXPwJgQI9LZAaXAgjB4oh/FD5TBxeCSMHRoJ4/FxIj5Pj6WhUssXuFRdCnMnK2H6+FyYPha/js9n61Pp8Yb436b5asvv7UACBPaOwEC8VEG5vXO/XSkBAgQIEOgKgV4Lyo3Gf8RKwayzzz67od+ll14aXvKSlzTcZ2PnBd75zneG3/zN31z3xs94xjPCX/zFX6zbnjYIyjVk2dGNnQ7K1U4+BeWe+cxnhvn5+dqmbXkUlNsaa7cH5f7pn/4pPOhBD1p3Uem/9eedd9667Tu5If3A5Kb3v3cYGBgIP/mHT+7kR/f0Z0383M3DnZ/5tHXXcNVfvjOc/N4P1223gQABAgQIECBAgAABAjWB0kh5dcCtPtA2MlIE3kazgFst5JZCbyOHDoSBwcHa23TF4+LcfFiIAbYUblsswm31z/P1ueWgWwq7Lc4UQbeqgElX3EQnQaAHBIZGSjEIl4fgagG4sRSMS9sOlkP8h62Wr2KxUg0zk/NhJobhpo7FANzkbAzFpUBc/IrhuBSWsxAgQKAdAUG5dvQcS4AAAQIECLQk0GtBuT/6oz8Kf/iHf9jwWv/t3/4tm2I2NTXVcL+NnRX49//+34ePfvSj6970iiuuCPe85z1Dtck/6AnKrSPb8Q3bFZRLF/KJT3wiPPzhDw+V+FvP27UIym1NttuDcu973/vCr//6r6+7qDQhNE2t3I2lFpC72f3ulU0h+N+f/WL44f/4+904lZ78zFs98qHh5g+476pzr85XwhUvf21IExEsBAgQIECAAAECBAj0r0A2ya02ta2Y1pZNdqutF/vybXnwbShOfysfOpj9klI3yeST2vLpbVmorQi9LSyH3vKJbtmUt2KqWwrEzR2b7KbLcC4ECPSBwMgZQ1kYLk2BS6G48cNpSlycDHdkJAyPtTcNszKzkE2CywNwxWS4bELcXJg94d9x+uDbxyUQ6GoBQbmuvj1OjgABAgQI9KdALwXlHvzgB4d//Md/DINNfjv0yU9+ckgTzizbLzA+Ph6uvPLKcM4556z6sKWlpfBLv/RL4XOf+9yq7fVPBOXqNXZnfTuDcumK3vCGN4RnPetZ23ZxgnJbo+32oFyz80th23K5HBYXd64uZm1AriYtKFeTOP3j0PhYuNvzL47THcZWvfjoV78Rvvvu9VXvq17kCQECBAgQIECAAAECuyqQpmlnFaUpzBant2UT2+oDbqu25VPdUn3p4Eg+0W1XT37Nhy/F/0+ZQmu1WtLliW6pxjQF3oqw26rXxH3zJ06EhenZNe/mKQECBLZXIKtI3Z/qUPMA3PJjEYZrpyI1xKlvsw0qUtNUuKmjqdrZBMvtvbvenQCBjQQE5TbSsY8AAQIECBDYFoFeCcrd7GY3C//6r/8a0mOjJQXo0oSzFNSybL/Aq171qvD85z9/3Qe9/e1vD0996lPXba/fIChXr7E762uDcqdOnQrvfe97wxe+8IXwox/9KPv68Y9/HE6ePBlufetbZ5Ma73jHO4a73vWu4UlPelJIFcinWy688MLw7ne/+3Qva2m/oNzW2JoF0d71rneFJz7xiVt7s2149Ste8Yrwwhe+sOE73/SmNw3XXXddw32d3NgsIFf7DEG5msTpH8/+tUeEm9znF9e98Jtvens49aOfrttuAwECBAgQIECAAAECnRMYGCzFqdhFgC2rLI3rcVpbvi1/zIJwy6/JJ7qVD+zL6k47dybtv1M1/tJUrbK0PuyWrRfT27LJbrUpb8W2ucmToTo31/4JeAcCBAh0WGCoXApj2TS4OAkuVqOm6XCdrEidvTGvQ52KAbhUlZqqUdO6itQO30hvR4BARwUE5TrK6c0IECBAgACBzQj0QlAuTZD72Mc+Fh70oAc1vKQU6Ln73e8eUk2fZfsFzj///PDFL35x3WS/ycnJLFB1/fXXb3gSgnIb8uzIzvS/pVSR+qlPfSq84x3vCKn6crOVxbe97W3D6173uvCrv/qrG57rD37wg+z7YTsqWAXlNqRft7Pbg3IpJJfCco2W8847L1x11VWNdnVk2+kCcrUPEZSrSWz8uP/254Q7PvXCdXVJR79yZfjuez648cH2EiBAgAABAgQIECAQSsNDWagtC7MV09uyqW4p7Fab7FYE4JYDcXGi28iBA6FUHu4qwWqlEoNucaJbDLJlE9zi+mIt1JY9Fs+LqW+1MNz85ImQjrUQIECgFwVGJmJFai0AV6tJTVPhzuxwRepkCsLlgbipGIhTkdqL3y3OmQCBJCAo5/uAAAECBAgQ2HGBXgjKvelNbwrPeMYzGtrMz8+HBzzgAeHzn/98w/02dlYghRaT9S/+4vppQalqM1Vunm4RlDud0M7sP+uss8LpQo0bncmjHvWo8Nd//dfhyJEjTV/2tKc9LaT73elFUG5rot0elPuDP/iDcOmllza8qDTJ8Jprrmm4r52Nmw3I1T5DUK4m0fxxeP++cJdn/+cwfMbEqhctzMyEK/+fv4z1RSdXbfeEAAECBAgQIECAQD8KpLDacoAthtuykFsWcFuZ7JYCb7Xw21AReisf2J+F5LrJZDH+m1ua6LZQC7jV15im7XVht2yyW5roFr/m4i9SLi2q8eume+lcCBDonMC2V6SeqmQBuOk0Ea6YCqcitXP3zzsRINB9AoJy3XdPnBEBAgQIEOh7gW4Pyr3oRS8Kf/Inf9L0Plx88cXhzW9+c9P9dnRW4JJLLgmvfe1r173pV7/61Sw8txhrMU63CMqdTqh39j/+8Y/fsF71K1/5SkgTCDu9CMptTbTbg3KvfvWrw3Of+9yGF3X48OFw/Pjxhvta2bjVgFwrn9Etx3zh9/94x05lIIaoz/3t3wz7zr7Vus+85u3vDpNXf3fddhsIECBAgAABAgQIdKPA4HJN6WgMvNUqS4v1uoluqwJwcaJbCrqlvxd307IqyJZCbDHotjzRLQu6rUx0ywNx6TWzYf74jWFpaambLsW5ECBAYEcFBodLcSpcrEY9NFo8jsT1cphIk+IOlEMopVhHa0u1Ug0zqSI1C8Lltah5IG4+pMlwS1X//W1N1lEECPSqgKBcr945502AAAECBHpYoJuDck9+8pOziVXNeFNgq1m4otkxtrcucPbZZ4crr7wyTEysnhaU3vH+979/+OxnP7upNxeU2xRTz7zogx/8YLjgggsanm/6h/Wb3OQmHa9FFpRryN10Y7cH5Zr9N2FhYSGUy+WO/YBm5PDBcO7vPjlWEu1vatVPO3YsKDcwEH7+cReEI//uLuv4/u3jnwk/+cdPrdtuAwECBAgQIECAAIFtEYh/Nx2KobU00S3/ajDRLU1zW64xrU12Gw3lgzHoFo/vliX9/+kUXkvBteWJbkWNab4tn96Wwm9ZIC6F4FL4LX7NT97YLZfhPAgQINC1Ao0qUsdiGO6MM0fD8NhQW+ddmV0MMzH0lgJwU9lUOBWpbYE6mACBvhYQlOvr2+viCBAgQIBAdwp0a1DuP/yH/xA+8IEPhKGhxv+n9G//9m/DE57whI4FKLrz7nTXWX3kIx8Jj3jEI9ad1Dvf+c6QQo2bXZqFYtLx5557brj66qs3+1Ze1wUCN7/5zcM3v/nNcPDgwYZn8xu/8Rvhve99b8N9rW4UlNuaXLcH5T70oQ+FdE/XLtdee21I31+dXEyU66Rm/l63euQvh5s/4D7r3vjY168K333X+/05vU7GBgIECBAgQIAAgWYCA6VSyGpJizrSVRPdYgAuqyktQm61ytK85jSf6NbsfXdj+1KcuL880S2b3pZPdEtBtuWpbssT3vLJbmlf5cTJLOy2G+fsMwkQINBPAgNx6lua/pYmwY3HSXDZ46H4GNfTZLhSnBrX8hKnvs2mitTjKwG4tD4dw3FTR2NoeU79dMu2DiRAYM8JCMrtuVvuggkQIECAwO4LdGNQLk0n+4d/+IcwNjbWEOgTn/hEeOQjHxnm5uYa7rex8wLNKjZPnDgR7njHO4YUaNnsIii3Waneed173vOe8LjHPa7hCb/mNa8Jz3ve8xrua3WjoNzW5Lo9KPeZz3wmm0q59qq+/vWvh7vd7W5rN3fk+VYDc//7s18MP/wff9+Rz+6nN7n5A+4bbvXIh667pJM/+HH41lv/JizFqYAWAgQIECBAgACBvSMwEH/ZsX6iW209qynNwm9FlWltols23S1O7tl/RhaC6yapamUhn+YWJ7otpkluWcCtVlWaT27LJ76t7EvBuPkbT4Tq3Hw3XYpzIUCAQN8KrKtIPVgLxcVAXFxXkdq3t96FESDQRwKCcn10M10KAQIECBDoFYFuC8qlUMSnPvWpptOpvvzlL4cHP/jB4eTJk71C3PPnOTw8HH7yk59kFZprL+aSSy4Jr3vd69Zu3vC5oNyGPD2586UvfWl42cte1vDcL7vssvCUpzyl4b5WNwrKbU2u24NyaSLhne50p3UXlULRD33o+hDWuhe2sWGzgTlBufXIZ97jF8JtH/ur63ZMX3td+NZfXJZNz1i30wYCBAgQIECAAIGuFijF//+fTXQr6kvXTm3L6kzX7Et/py7v3xdK5eGuurbqfCULt+VT3fJpbsuVpVn4rag0nSsei21Z0C2G5CwECBAg0B0CqSJ1LJsEVw4T8XF5/UicJjre3p89C7EiNdWjZl/H4kS4rCY1r0udvbHSHQDOggABAn0uICjX5zfY5REgQIAAgW4U6Kag3G1ve9vwz//8z02r9q655pps6tD111/fjZR9e0779u0LaXJco+WNb3xjWNjixKCHPexh4bzzzmv0diGFqo4fP75uX/qcb3/72+u229AdAo997GOb1qumyt5HPepRHT1RQbmtcXZ7UO6GG24IR44cWXdRqWI7TbPcieV0gTlBudV34dCd7xhu98THhlRjUr/MHZ8M33zTO0Ll5Kn6zdYJECBAgAABAgR2SKA0EutJaxWlMcC2uro0f56H34oa0+w1Meh2YH8oDQ3u0Flu7mMWYy1pFnJbO9Ftuca0mOqW6kuLaW/ZRLfJG8PSosq7zSl7FQECBHZfoL4idSzVoqaq1CIQd0YMw21bRWqsSU1BOQsBAgQI7K6AoNzu+vt0AgQIECCwJwW6JSh3i1vcIgvJnXPOOQ3vw09/+tNwv/vdL/zwhz9suN/G7RPYKCi3fZ+6+p1/+Zd/OXz84x9fvdGzrhG4y13uElJNZqPlc5/7XLjvfe/baFfL2wTltkbXzUG5W93qVuFHP/pRwwt6+ctfHtK0wp1cmgXmBOVW7sL+nz873PGpTwgDg6t/kFo5NRWuevM7wuzR9WHnlaOtESBAgAABAgQIbCSQ/j66ampbMb0tbcv2xf1DqbY0qzFN6/H1cVv5wL74Swyljd56R/ctxU/Lwmu1UFsKssX60sVietvylLe0vTbRLQu7xerSyRNhaSm9g4UAAQIE+kVgpSI1VqKm6XApDBdDcdl6uxWpC9Uwc2OaBhe/YvitNhWu9ry66M+Ufvk+ch0ECPSngKBcf95XV0WAAAECBLpaoBuCcocPHw6f/vSnw53vfOeGVmna0AMe8IBw1VVXNdxv4/YKCMptr28/vHv63+6VV17Z8FK+9KUvhXve854N97W6UVBua3LdHJT7T//pP4XLL7+84QWl6ZMf+9jHGu7b7o1rA3OCcrn4xK1uEe70278Za7XKq27BQvyh5rfecllItasWAgQIECBAgMBeFUhBtVrIbTnwlkJtWbitCLoth9zqJrqloFusLh0YSD8i6o5lqVrNakvzSW15bWn6O1825a0ItK3UmOYT3RaKyW6putRCgAABAntPoL4iNQ/DxSDc4ViXul0VqSkYF6tSZybn9x62KyZAgEAfCQjK9dHNdCkECBAgQKBXBHY7KHfGGWdkk8Luda97NSQ7efJkeMhDHhJS2MayOwKCcrvj3kuf+pjHPCa8//3vb3jKKeiUAk+dXATltqbZzUG5V7/61eG5z33uuguqVCrh4MGDYXp6et2+ndxQC8ylH1r+5B8+uZMf3XWfNXbTs8Kdnv7kOMlkbNW5Vecr4Vtv/X/DqR/9dNV2TwgQIECAAAECvSYwEKtHswlt2ZS2VGFaX1Vam/RWTHRbsy8F3bppqS4sxlBbmuIW60njYxZyKya6pclueegt31+rOE3bKidOZmG4broW50KAAAEC3SGQV6QO57WoaypSUxguTY1reYmTROdOLSxPg5tKAbhjeRDu1FEVqS27OpAAAQI9ICAo1wM3ySkSIECAAIF+E9jNoNzIyEj4yEc+kgXhGrnOzMyEhz/84dm0uUb7bdsZAUG5nXHu5U950YteFP7kT/6k4SW84x3vCBdddFHDfa1uFJTbmlw3B+U+85nPhPvf//7rLuhf/uVfwi/90i+t227D7giMHDkUznv6U8LwvjNWncDS4mK4+u3vDie+84NV2z0hQIAAAQIECOyGQGl4OJveNhintqWQWx56yye6rVSXxucjqa505TXp7zjpmG5aqvEXR/JwWx50S2G2PPSWP9aH4JanvMUA3PyJEyH9IoOFAAECBAi0IlCKYbeJ+lrU5fVYlXow/llZan36aXVdRWoxES6G4qZiGE5Fait3zDEECBDofQFBud6/h66AAAECBAj0nMBuBeWGhobC+973vnDBBRc0NEvThNK5pSDdbi73vve9wwtf+MLTnsK1114bLr744tO+rhdfsNtBuRSYvPvd7x6+9a1vtcz38pe/vGm1b/2bvvWtb93177n68+mV9csuuyw86UlPani6l156aXjJS17ScF+rGwXltibXrUG59OfAifiDvLE1E8rS1b3iFa8IKYBp2X2BNB3lTs/4rTBy6MCqk0l1XN955/vC8auuWbXdEwIECBAgQIBAqwKlkXIebstCbrWAW22yWz7RbXnKWzHRLQXgygf2hRSS66ZlcW4+m+SWVZcWU93SJLcs4BZDbykElya95dWm+fbaRLdqZaGbLsW5ECBAgECfCZQnhrKpcKkWdVVFapwSV55o78/ThbnF5alw06ka9dhcfK4itc++hVwOAQIEOiogKNdRTm9GgAABAgQIbEZgN4JypVIppGDNE5/4xIanWI0/fH/84x8f3vve9zbcv5Mbm/msPYfvfve74Xa3u93azX3z/PDhwx27lte//vXhwgsvbPh+aYLU1VdfvWrf/Px8OHXq1KptW33yyU9+MjzwgQ887WHPetazwhve8IbTvs4LVgTS90YKMZ511lkrG+vWnvCEJ4T3vOc9dVvaXxWU25phtwblUgD2y1/+csOLecQjHhE++tGPNtxn484JDE2MZ5PkRs86supDl2Ilyvfe88Fw9KvfWLXdEwIECBAgQGBvC9QmuaXw2mAKu8Wpbfn0tuJ53L6yLQ/CpalvwzGYX4q1p92yLMUTWQ6wrQq0pXBbMdktbs9Db6nWtAjAFRPdlhar3XIpzoMAAQIE9qBAqkgd3R8rUmPwbTxOhJs4NBLGslDcaOhoRepyEC6F4eJUuBvmQmV2cQ+Ku2QCBAgQaEdAUK4dPccSIECAAAECLQk0C4JNTk6GQ4cOtfSepzvozW9+c3j605/e8GXph+9Pe9rTwtvf/vaG+3d6YzOftefR70G5tdfbzvO3ve1tTas4zz333HVBuXY+q3asoFxNovOPG93PxVjLmAJ0x48f7+gHC8ptjbNbg3LPfvazw5//+Z+vu5j0fZP+/Dl58uS6fTbsnED6QfedfudJYfwWN1v3oT94/9+F677wr+u220CAAAECBAj0rsBA/IW29Od/FnBLQbdivT78llWZFpPcssluKQgXX5sm0Kbju2VJk2+X60iLqW359LY4yS09Xxd2K8JvcV/lxpMh/buEhQABAgQIdLPAULkUxg7GiXBZGC4PxKX1sVSVGitSU1iu1SWrSD0xH2aO5ZPgporHmRSGixPiqgv+nGzV1nEECBAgsF5AUG69iS0ECBAgQIDANgs0C4JtV1Dula98ZXjBC17Q9Kqe85znhP/6X/9r0/07vaOZz9rzEJRbK9L8+UbBKkG55m7duOchD3lI+PjHP9701D7zmc+EBzzgAU33t7pDUG5rct0alPvsZz8b0hTJtUuaJJcmyll2V+D2T/6NcOi8O6w7iWqsRr/2M59ft30zG2auuyEc/cqVm3mp1xAgQIAAAQJbFBiIE9kGR4qa0izMtjK9LZvitrwtrzCtBeHyiW5nhIGB1n+gvsVTPe3LqwuLqypK03S3POCWqkprNaW1KW4r+9Jkt8oJv2xxWmAvIECAAIGeECiPx4rUVfWoK4G4kTM6UZGagnCzWSBuKobgalWpMzfOhyAL1xPfI06SAAEC/SAgKNcPd9E1ECBAgACBHhNoFgTbjqDci1/84vDHf/zHTYVe8pKXhEsvvbTp/t3Y0cxn7bkIyq0Vaf5cUK65zXbvue997xsuv/zy8I53vCO89a1vDT/84Q9b/sj73Oc+WaXqbW5zm6bvkeqV3/WudzXd3+oOQbmtyXVjUO62t71tSP/dbLRccMEF4cMf/nCjXbbtoMCdn/Wfw8Qt10+Ta+cUjn/z6vDty3a/Vr2da3AsAQIECBDYLoHS8PDyFLdscluc6JZNbatNeSuCblmtaW1fnOiWBd32nbFdp9XS+6ZgfQq0pXBbPsktBdzSei3cVgu75fWlterSyslT2bS3lj7UQQQIECBAoMcEGlakpolwcTLcRPwaLLdRSx6no85NLYTpOAGuFoBL9agqUnvsm8TpEiBAYA8ICMrtgZvsEgkQIECAQLcJNAuCdToo96xnPWvDSXGveMUrwote9KJu4wnNfNaeqKDcWpHmzwXlmtts9540vStN8UpLNdYRpcldb3nLW8Lf/d3fhVR3uZnlzDPPDGky5EUXXbTh1ImvfvWr4fzzz9+W2iJBuc3cqZXXdGNQ7qUvfWl42ctetnKSxdqPf/zjcM4552z6+3HdG9jQMQFBuY5ReiMCBAgQ2CMCpZHy8kS3POAWJ7cV1aRDyzWmacpbXmtaqzEdjiG3wXhsNy2Lc/PZRLdagG15olsMu2WVplmdaQzCZc/zbek18ydPhup8pZsuxbkQIECAAIFdFahVpI4dStPgUgAuBuHi41h6bLcidbEaZuP0t5UgXF6VmsJxKlJ39bb7cAIECBDYgoCg3BawvJQAAQIECBDojECzIFgng3IHDhwIx48f3zBU8/Wvf72jgZo//MM/7MhEomY+a/UF5daKNH8uKNfcZrv31Afl6j/rJz/5SfjYxz4Wvv/976/6+tnPfhZucYtbhDvc4Q7ZV6rGffKTnxwOHTpUf/i69RTC+5Vf+ZUNa1nXHbSFDYJyW8CKL+3GoNw111wTbn/726+7kBSge/nLX75uuw07LyAot/PmPpEAAQIEdlcgqyIdLYJs9WG2NRPd8oBbnPKSvXYkpKBbmgbXTUseYFs9tW15otuqyW5xylsRfluM4bf5E6fC0sJCN12KcyFAgAABAl0vUF+ROhYnwk2kUFycCJcCcSP72vs7wuL8Ygy9qUjt+m8CJ0iAAAECLQsIyrVM50ACBAgQIECgVYFmQbBOBuUOHz4cjh492uoptnTcb/3Wb4W//uu/bunY+oOa+dS/Jq0Lyq0Vaf5cUK65zXbvaRaU6/TnPv/5zw+vfvWrO/22y+8nKLdMsamVbgvK3fve9w6f+9zn1p37Qvyh7K1vfeuQApqW3Re4w0WPDwfveLuOnsh1n78i/OADH+noe3ozAgQIECCQBAYGBuL0tnxSWx5gS9Pc6p4vV5Wmbfn22tS34f0x6DbYRrVZh2/BUvylkzS1LQXXVk10ywJuK9PbssluWcitmPIW1ysnToZ0vIUAAQIECBDonMByRWoKwBXVqLXHTlSkzseK1KlUi5oF4vJ61LQ+dXQuVGYE2Dt3J70TAQIECHSjgKBcN94V50SAAAECBPpcoFkQTFAuv/EXXnhh+Ju/+ZvTfhcIyp2WaPkFuxGU+8IXvhDuec97Lp9Ds5VUEfyGN7yh2e6e374TQbk3v/nN4eKLL95WK0G5rfF2W1Du9a9/fXjmM5+57iL++3//7+Gxj33suu02ECBAgAABAv0vMDBYyie0pRBbCrjFr6yuNHs+EteL7fGxVl1aP9FtoFTqGqTq4mJI09uysFtduC2b3FYE2xaKIFw25a14bdq/cDJOdFta6pprcSIECBAgQGCvCAwOl7IQ3NjBWIlaTIPLH/Oq1BSWa3WpNqtIjeG4FIarLvizv1VbxxEgQIBA7wsIyvX+PXQFBAgQIECg5wQE5Ta+ZW984xs3Ffq57LLLwlOe8pSN38zeTGCng3ITExNZ9e/waeqQ0g+k7nGPe4Qrrriib+/Udgbl5ufnw+/93u+F9L+Z7V4E5bYm3E1BudH4w+0f//jH4cwzz1x3EQ972MOyCuB1O2wgQIAAAQIEul6gNDy0PKmtNtGtPtA2OLY67FZ7ngJwQ/smsolw3XKR1UolBt3mwsJcDLulxyLclleXrgTgahPdsv1zcQJcfF0lBt0sBAgQIECAQHcKDI8PxVrUPPg2dnh7KlJn0mS4FIA7Ph9mjhXT4SbnQ5CF685vCmdFgAABArsuICi367fACRAgQIAAgb0nICi38T3/5je/Ge50pztt+KJK/EHKueeeG773ve9t+Do7c4GdDsr9yq/8Svj7v//70/K///3vD7/+679+2tf18gvuc5/7hH/5l3/p+CV88YtfzCaEpcl9O7EIym1NuZuCcs95znPC6173unUX8PnPfz7c9773NUFlnYwNBAgQIEBgZwRK5eF8olsMtNWCbnl1af1kt7XT3PIK0+EzJnbmJDf5KYvxFzjyUFseXqsF2lKVadpeX2maTXZb3hYnuk1Nb/JTvIwAAQIECBDoNoE09W1kXwrDxYrU5alweTBu4kj8O065jar1+Au2KlK77Y47HwIECBDoBwFBuX64i66BAAECBAj0mMBOBOXSBKHrrrsu7Nu3b8d0HvGIR4SPfvSjbX3ezW52s/Czn/3stO+RQii/+7u/e9rXeUEu8Kd/+qfh93//99dxpMDhLW95y3D99dev29fOhmafV/+e1Wo1/MIv/EK48sor6zf35fptbnObkMKDaXrXQx/60HD48OGWrnNhYSF8/OMfD6961avCJz7xiZbeo9WDBOW2JtctQbmxsbEsUJz+27p2ud/97hf+1//6X2s3e06AAAECBAhsUmBwtAi4pcdUXVpfUZo9zye6ZVPeiufpNcNxmttgubzJT9mZly1kgbYYcms00S2F2uL0tnzKWx6Eq010q5w4FdI0OAsBAgQIECDQvwKpIjWrR02T4ZbDcDEYl57H2tRU597qklekVrKJcNPZNLj5fF1FaqukjiNAgAABAqcVEJQ7LZEXECBAgAABAp0WaBaUW1xcDGnCT7PlhS98Yfjnf/7nZrv7YvvjH//48O53v3vDa5mLP6S5/e1vn1UJbvhCO3dN4HOf+1y4973vveHnp/t84YUXbviaftxZKpWyutl73eteIYWX0tdNb3rT7DEF6AYHB8M3vvGNLMB47NixcPTo0XDNNdeET33qU1mo6dSp7auWuvTSS8ODHvSghuzp3NIUx7XLiRMnwoEDB9Zubuv5Pf7ohauOTwHDZp8xEaepNAoEp6Dw5X97+ar32ejJl176yo12N9z3T//0T6Hc5IfcP//zP5/d17UHvutd7wpPfOIT127etueXXHJJeO1rX7vu/S+//PLwuMc9bt12GwgQIECAwJ4RGBgIQ8Ukt+XAWwqzZYG2YqJbXB+qD8HFkFvaP3zGeCgND3cN1VKctrI8tW22mOiW6ktT+C1+rQq4ZdtqU99mw/zJqbAUfxnDQoAAAQIECOxtgeGxoTz4lgXhYkVqfBxLU+JiGG50f3sB/8X5xTB1LNaiFhWp07EidTkUNzmnInVvf+u5egIECBDYBQFBuV1A95EECBAgQGCvCzQLyp3O5YILLggf/vCHT/eynt7fbApT/UW9/vWvD89+9rPrN1nvIoH9+/eHFPBKga9mSwqFnnfeeVkArNlrbN95gfe9731brsLdiaDcEy58Qjhy5MiWQHYiKDcbf9A8MjKypfPayaBcmib3/e9/f11gL4WNU+jxBz/4wZbO3YsJECBAgEA3CQzEXz5IobWhLLyWT3ZbnugWA3DZ9iLklr8mryzNg24TobTB31V3+jqX4t+Nlye6FUG2FG7LA25FdWltolsMwNWH3yonT4WlOKnZQoAAAQIECBDYSCD+jkAY2T+8PRWp8YPnT1XCVArCFYG42vpUnBJXmRbK3+je2EeAAAECBHZaQFBup8V9HgECBAgQIBAE5Zp/E3z7298Ot7vd7Zq+YHp6OqRJTddee23T19ixuwKPetSjwv/8n/9zw5N4+9vfHp761Kdu+Bo7d15AUG5r5t0elPu93/u98JrXvGbdRb3yla9sWMW87oU2ECBAgACBbRQYGBpaNdEtqyddri5dCbUtT3RLobgYUK8F3VJQrluWamUhD6/VhdwWi0BbfagtTX2rTXfLp73FqW8p6BYnwlkIECBAgAABAu0KZBWpB1I9avxK0+CK6XDZepwM17GK1DQRrgjFTR+fzabFVSuC++3eP8cTIECAAIGdEhCU2ylpn0OAAAECBAgsCwjKLVOsWvm5n/u509ap/tmf/Vl4wQtesOo4T7pL4NWvfnV47nOf2/SkKpVKuMMd7mCaVVOh3dshKLc1+24Oyo2Pj2fT5G5yk5usuqg0aS9VV6dJgBYCBAgQINCOQKoezSa6FfWlK1PbYqCtVmm6Zl8t9DY0MR4G0liTLlhSRG1pvpIF2GpVpYtFfemqiW5xW9q/UAvAxdBbel3l1FQXXIVTIECAAAECBPaKQMOK1IMpFNd+RerC3GKYmazVosbpcHUVqTOxIlW2f698l7lOAgQIEOh3AUG5fr/Dro8AAQIECHShwKMf/ejwoQ99aMtn1u/Vq0960pPCZZdd1tTl5MmT4ba3vW244YYbmr7Gjt0X+PKXvxzufve7Nz2RN7/5zeHiiy9uut+O3RN4y1veEn7nd35nSyewE9Wrj/mPjwm3vOUtt3ReO1G9+tOf/jTc4ha32NJ57VT16uMf//jw7ne/e925Pf3pTw/pPlsIECBAgEApTmdLgbb6itKV6tI40S3bHx9rr4kT3dK2bKJbDLp105ICa8shtxhky9ZToC1NcMumvKXH/Hnatvz69No4sdpCgAABAgQIEOgWgawidV+sSI3T4MayqXDlvC41rk+cGf8uVh5s61TnpypZPWo2ES5OhUvVqDNFIG5uSkVqW7gOJkCAAAECPSIgKNcjN8ppEiBAgACBfhJI0xPOP//8UC6Xt3RZV199dTh+/PiWjumlF7/tbW8LF110UdNTfvnLXx5e+tKXNt1vx+4LHDp0KAsylppUYaUJXKlaNwWMLN0nkKaQ3e1ud9vSiS0sLIQvfelLWzrmdC/+xZe9YNWUmZHRkXDw4MHTHbZqf5pceOzosVXbmj1JdWdf/r9f1Wx30+0pJHfrW9+66f5GO1LQ9zvf+U6jXR3dlv43eM973nOVY/qAL37xi2FxcbGjn+XNCBAgQGB3BGpVpSnIlgfc8sflsFtWY1psS7Wl8flg/DN1+IyJUCoP785JN/jUNNEtC68th9qKQFsxvS0PvaVwW/6VTXTLwm6zcZrbdKjGP/MtBAgQIECAAIFeEigNl8J4XUVqCsRNxGrUWlVquxWpcycqWQAumwinIrWXvjWcKwECBAgQ2BEBQbkdYfYhBAgQIECAAIHTC3z/+98PZ599dsMXpoBgmiY3OTnZcL+N3SGQph5+8IMfbHoyr3vd68Ill1zSdL8dBJLA3f/L80OzsOV2CFWr1XDFf/mz7Xhr70mAAAECBBoKDMRAcwq0ZdPa6ia1ZVWmWcBtZXrbqoluKeg2EYNuw0MN33c3Ni7FP0ezitIivLZqWtvyttWVpbXpbpWpqbC0IMC9G/fNZxIgQIAAAQLbKzA8OrgcfMsDcLEetZgQN7p/a788vfZMlytSUwhuOQgX14/Nx+pUFalrvTwnQIAAAQIEVgsIyq328IwAAQIECBAgsCsC55xzTvje977X9LNf8pKXhEsvvbTpfju6Q+DP//zPw7Of/eyGJzMVfxCawo6pEtNCYCOBu7/0uaE0tHMBgGqcinfFH71mo1OyjwABAgQIrBIYGBosJrTlYbfaRLdsalsKvhXT27Kpb9nzWsXpaJzoNh4GBturzFp1Mm0+qcagWjatrTbRrVZdmia4pfXlsNtKlWm+Le47FYNuMShnIUCAAAECBAjsNYF1FalpIlytKvVInOY70t7f95pWpMZg3NwpFal77fvN9RIgQIAAgU4KCMp1UtN7ESBAgAABAgRaFHjqU58a/tt/+28Nj77++uuzgNWpU6ca7rexewS+9rWvhbve9a4NT+gVr3hFeNGLXtRwn40E6gXOf/H/FQZHRuo3bev64txc+NdLX7etn+HNCRAgQKC7BErDw3GaWwq0xQBbMdFtJdQWf7BZ7Kuf+larLh2aiEG3JjXzO32VqbZ0KVaP1ia6ZTWls7VA20qFaao2zaa4ZXWmeQAuheMqU9PxDdK7WAgQIECAAAECBNYKpIrUsViRWqtFra9IHYvBuNJgae0hm36+tFgNs6kiNQbfZo7Px2lwc3E9f0zrixW/jLBpTC8kQIAAAQIEtiQgKLclLi8mQIAAAQIECGyPwJve9KbwjGc8o+GbP+95zwuveY1pTw1xumjj+Ph4uPHGG8NQg0lgJ06cCGlq4LFjx7rojJ1Ktwrc7fn/ZyjvO2PHTm/+5KnwtT974459ng8iQIAAgTYF4viOUnk4n+gWA21DMexWH2jLp7mthODWhd5idWn6B8FuWJZiSK06H4Nu2fS2FGCLXzHotvw8rddNessmv9WF3RamY9DNQoAAAQIECBAg0LJAqkgdOzySh+HSRLg0GS4+T9PhRmNIrp1lcX4xVqPG8NvaitS4bSZu8/sK7eg6lgABAgQIEGhVQFCuVTnHESBAgAABAgQ6KHDmmWeG+9///qEaq5vSDwxrX+n5Jz/5yTAzM9PBT/NW2yVw73vfO9zsZjfL7l/9vbz22mvDFVdcsV0f6337TODOz/rPYeysIzt2VTPXHw3feP1bd+zzfBABAgQIhJVJbqmitDbZrbaehd+KoFu2LQbh6reNj3UVYa2GNJ/aloJueeAtm+CWQm5Z+K0IwNXWY6Vpel3aZyFAgAABAgQIENg+gVpFal6LWgThahWph9MvXXSgIrUWhjtWhOLiRLgUjlORun331TsTIECAAAECrQsIyrVu50gCBAgQIECAAAECBAh0XOAOFz0h7D/n1h1/32ZveOL7PwzXvP09zXbbToAAAQJrBAbiTxuzcFtdeG057JaF3tIPHFMALp/0llea5uvDsbY01Z52y7IUfykjm+C2HGBbCbStqjHN9ufhtlogbiHWllYrC91yKc6DAAECBAgQILBnBUpDA2HsYJwEVwTgxg+OhonD5ZCqUsfjY0crUtN0uCIQN3VUReqe/aZz4QQIECBAoIcFBOV6+OY5dQIECBAgQIAAAQIE+k/gNo9+eDjrHr+wYxd2/Ze+Gn744Y/u2Of5IAIECOy2wMBgKasqzQNsaXLbSn3pUAq3FWG3bHu2vjLRbSgF3Ybam7rRyeuvLi6u1JWmMFv6msvrSvMa0zS1rQi41SpLs8f4mqmpsLRY7eTpeC8CBAgQIECAAIFtEtjuitSZyfkwlSbDxWlwqRZ1KpsKFytS4/al6tI2XZW3JUCAAAECBAjsvICg3M6b+0QCBAgQIECAAAECBAg0FThy97uFc37tEU33d3rH9z/4/4WjV3yt02/r/QgQILBtAqXhoSzctqqOtD7Qthx0Wz3RLU15G4q1pQODXRR0q1RiuC2G1lJ4LX7l4bYYbIuBt/x5HnKr1Zuu1JjG/VMz8YeWgm7b9o3mjQkQIECAAAECOykQf2I7um+4mApXV5F6qBwmjnS4IrUIxKV61Om4PneyspNX6rMIECBAgAABArsqICi3q/w+nAABAgQIECBAgAABAqsF0rSiuz3v4liNsv1BjjSJ6GuvflMMW0yvPgnPCBAgsI0CpfLw8kS3bKpbfU3p6MpEt3zfSFFjmleZDo3FoFuptI1nt/m3TnM1qvPzeaitNrUtC7gVE9zqwm5ZwC1OesuCcMX2hemZEJZM59i8uFcSIECAAAECBHpboHlFaqxNPTzSXkVqnPo2e6ISg2+pGjUF4FSk9vZ3i7MnQIAAAQIEtktAUG67ZL0vAQIECBAgQIAAAQIEWhQ45z8+Khz5d3dp8ejNH3b0K1eG77//7zZ/gFcSILDnBQYGBkJppLwcdMtrSoswWxFyq4XfhlaF3vLpboNpoluXKKZpbItZ0K1+aluc6jabB9pSuG0hrddNels10W16tkuuxGkQIECAAAECBAh0i8DQ6GA2FW7icDmMHconw03EENx4XB/dPxxC/Pt0q8vi/GKYuTHVo8avIhCX6lJTVWqaDKcitVVZxxEgQIAAAQJ7SUBQbi/dbddKgAABAgQIECBAgEBPCJQP7g93edZvh1QvuF1LtbIQrnz9X4X5yRPb9RHelwCBbhSIP5gbGitCa6mKNNWU1mpL0/MYblu3rVZlGo9L9aXdsmRBtyLIttBgolsWaothtxR8q5/othJ2m+uWS3EeBAgQIECAAAECvSIQf7K6tiI1BeImYkXqeKxIHY5BuXaW+elKDL7Nh6lsKlwKxRXT4VSktsPqWAIECBAgQIDAsoCg3DKFFQIECBAgQIAAAQIECHSPwE3uc49w60c+dNtO6Ecf+Xi47nNf2rb398YECGyPQKodTcG2FFjLAm7ZehF8K4JsefAtD70tr6d9cZpbaThOseiSJdU/59PaigluaXJbLdRWTHFrNNEtD8DNhBT4tRAgQIAAAQIECBDotEBWkXogr0MdTwG4NBkumwrXoYrUk7EitRaAq02Hi1Phpm6Ify+uVDt9Od6PAAECBAgQIECgTkBQrg7DKgECBAgQIECAAAECBLpJ4Oz/+Mhw5r+7a8dP6YavfD384P0f6fj7ekMCBE4vMDA0tG6iW15V2ijsFrcVobgsGJeCbkPtTag4/Rlu/hXVSiUPtmWhtmJqWxF2SwG4dRPdUgiuFoCbng5Li34IuHltryRAgAABAgQIEOikwKqK1IMpCLcSiBtTkdpJau9FgAABAgQIEOgqAUG5rrodToYAAQIECBAgQIAAAQIrAmly1G1+7eEdDculkNwPP/jRkCoLLQQIbF0gTWTLwmtFfelyTelIqjGthd3Sepz4NlIE3dJ6qjSNQbf0v+tuWJbiSVTn58NirCythddWTXOrBdqKStP6yW61AJz/jnTDnXQOBAgQIECAAAECDQXiT0BHzhjOAnATaSJc/BqLU+E6VZFamVnIpsJNxUrUmWI63FScCjcdJ8TNxYlxFgIECBAgQIAAge4UEJTrzvvirAgQIECAAAECBAgQILAskGpYf+5hD4yViUPL27a6kioKf/KPn1K3ulU4r+8vgYGBUCqXs0DbcnVprDBdnuhWC7St21a8Ju4fiO/RDcvS0lJYnIvVTHFKW62+NAuwZQG3IvxWm+qWXjNT1JouB+BmQ4jvYSFAgAABAgQIECDQqwK1itSxGILLAnBZPWoxHS6ulwZb/yWVpepSmG1YkTofpo/G6cnzfvmsV79vnDcBAgQIECCwtwUE5fb2/Xf1BAgQIECAAAECBAj0iED54P5wy4f8H+HQXe8U/7F/89WL1cXFcPzrV4WffuIzYX7yRI9crdMk0FggTWMbHCnH6WxxQtu6iW5xYlsKusWvoTi9LT2m160E4vJt3RFzixm1ONUxD7nVTXSrTXerqyhNAbds4tty2K2oNI3PLQQIECBAgAABAgT6XWBopBSnwuUT4dJUuG2pSI1T4aaLqXDTy+vz8e/sfrGk37+/XB8BAgQIECCw9wQE5fbePXfFBAgQIECAAAECBAj0sMDwxHg4eOc7homfu3kYO+vMMLx/XxYEGhgqhaWFahaoqZw4GWauvyFM/eRnYfIbV4fK1HQPX7FT7yeBLOhWhNzyKW5FmC0LtRVBtyLklgXcaoG3LPwWXxtDct2yLMUQal5ZWkx0S+G2FGorJrdlU95qE93q60uL4Ft1br5bLsV5ECBAgAABAgQIENg9gfiTyqwi9VA5q0fNQ3HlMJHCcfFreKz1yerpotZVpE6uhOJmT6hI3b0b75MJECBAgAABArsjICi3O+4+lQABAgQIECBAgAABAgQI9JzAwNBQPq1tTdhtubo0214/0S2G32rbYtitNDzcNdec6ojzMFs+0S0LuBWBtjTpbaXGdCX8VpvstjA9E4OpC11zLU6EAAECBAgQIECAQDcLlAYHwtjBcmhYkRqnxJXiL361uqSpb3OpIvX4XJjKpsLNh5lsPQXi4t/l51SktmrrOAIECBAgQIBAPwoIyvXjXXVNBAgQIECAAAECBAgQIECggUAKquWVpPn0tlqILasyjdPb8urSWFEaw235vpWgWzpuK7W/DT6+o5uq85XlMFstwJbVlNZPdCumty3UAnC1yW4p6BarTy0ECBAgQIAAAQIECHRGIKtITdWoa+pR0/OxA/EXZgbSjyRbWxYr1TAzORfDcCn8Fr8mZ/PHGIhLz1WktubqKAIECBAgQIDAXhQQlNuLd901EyBAgAABAgQIECBAgEBPCpRGUnAtBtmyKtL8MQu1xeeDaXsKuKX1sSLgVmzLXh+3p+rTbliW4klU09S2WE2aprdlAbe0noJsKeg2V9SYZtvS9vx1y1WnMzHotpTexUKAAAECBAgQIECAwE4JjOwbjkG47a1IzcJwKQCXheLS41xQkbpTd9jnECBAgAABAgT6X0BQrv/vsSskQIAAAQIECBAgQIAAgS4QGIgTFPIwWx5oy8NrxXqtnjQLuq3sT69fmfo2EocwtD6FoZMEaRrb4tz8mgBbCrOlmtIYdsse8+fL2+omvaVaUwsBAgQIECBAgAABAt0lkCpSRw+kIFw5TByOk+Cy6XAjYSKF4+LzdipSQ6xInS0qUqeLitSsLjUF4o6qSO2u7wRnQ4AAAQIECBDoXwFBuf69t66MAAECBAgQIECAAAECBDookKaxZRWl9dPasultRU1pfait9pr4WKsxLcX93RFzC2FpcTFOc1uZ3lY/0S2F2NJEt2xbFm6Lz+Nj9voiCFeN+y0ECBAgQIAAAQIECPSeQKOK1LEUjEsVqQfLHahInQ8zcQrclIrU3vvmcMYECBAgQIAAgT0gICi3B26ySyRAgAABAgQIECBAgACB+POeocF8OluqI82qSlNN6eqJbSvVpXVVpkWdaak83DWM1YXFbGrbcoAtC7IVE9yWJ7rlk91qIbgsAFeE46qVStdcixMhQIAAAQIECBAgQKCzAiNnDMWpcCPZFLj8Ma9LnTgyEobHhtr6sMrMQlaHOp2CcCpS27J0MAECBAgQIECAwM4LCMrtvLlPJECAAAECBAgQIECAAIEWBErDw0XArQi3pQluxUS3POBWX2Na7KtNeYtht9Jwez8QauGUmx6SgmpZcC0F3IrpbfWht8UUaKurL81fG7fNpWrT2bAUg3IWAgQIECBAgAABAgT2psBAaSCb/rZSkZoH4VIoLlWmloZLrcOoSG3dzpEECBAgQIAAAQJdLyAo1/W3yAkSIECAAAECBAgQIEBgbwmMnnUknH3Bw2NlaRF8K6a+DQwOdgXEUjyL6tx8rCLNK0mzUFttolt8TM/zffFxZiZ7vhJ6yytNl6rVrrgWJ0GAAAECBAgQIECAQHcKDJVLYayYCjcRq1HHYwBuuSL1QKxIjWG5VpdqpRpmbowT4Y7FitTjeVVqbT09LsWwnIUAAQIECBAgQIBAPwoIyvXjXXVNBAgQIECAAAECBAgQ6GGBsZueFe56ye9u2xUsLS0V4bU8tJaF2orQ20JRTZo91rZl4bcUiqtNdJsLIb6HhQABAgQIECBAgAABAu0I1CpSswBcDMJlNalpKtyZKlLbcXUsAQIECBAgQIAAgWYCgnLNZGwnQIAAAQIECBAgQIAAgV0RGN6/L5z/B89p+tlpGls2ta0WYKuF22pT3Yrnq15Tvy/utxAgQIAAAQIECBAgQGC7BbKK1Dj9bfxwUY1ae+xUReqpSpwKFyfDHZ/Lv+I0uOk4IW7q6GxYmDPFervvr/cnQIAAAQIECBDoPQFBud67Z86YAAECBAgQIECAAAECfS0wMDQYzrrn+TEMl1ebLhSBuIU00S1uq85X+vr6XRwBAgQIECBAgAABAr0jkFekpiDc6EogLlalTqSq1O2oSE2huBiOm1KR2jvfJM6UAAECBAgQIECgawQE5brmVjgRAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBbhMYmRiKIbgYfCsCcLWK1PEj5VAeH27rdCuzi2EmmwQ3F6aKENxMfExBuNkTfkmoLVwHEyBAgAABAgQIEFgjICi3BsRTAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBvSOwXJEag3ApEDeeHmM9arYenw8Ol1rHqC6F2bUVqbEedToG4VIYbiEG5SwECBAgQIAAAQIECOyMgKDczjj7FAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgV0SGBophbGD21SRulANM5Mx/JYmwhUhuGw9VqSmx+ri0i5dtY8lQIAAAQIECBAgQKBeQFCuXsM6AQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBATwqkitSxbBJcOUzEx/r18hkdqkidjGG4IgCXTYWLQbjZG1Wk9uQ3jJMmQIAAAQIECBDYcwKCcnvulrtgAgQIECBAgAABAgQIECBAgAABAgQIECBAgEDvCTSrSE2BuIkjbVakLsWK1JOVbCLcTAy/pVrU5elwcb2iIrX3vmGcMQECBAgQIECAAIE1AoJya0A8JUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQ2B2BlYrUkTCepsMdinWph/P1VJ2awnKtLtVUkXpjqkONX1kQLk2HWwnEqUhtVdZxBAgQIECAAAECBHpDQFCuN+6TsyRAgAABAgQIECBAgAABAgQIECBAgAABAgQI9IXAdlakLsTJb9NxIlz2tbYi9USsSF3qC0IXQYAAAQIECBAgQIBACwKCci2gOYQAAQIE8GHjqwAAQABJREFUCBAgQIAAAQIECBAgQIAAAQIECBAgQKCxQF6ROpxNhBuL0+Am0lS4OB0uq0g9XA6D5cHGB25ma6xInTu1UAThYkVqDMXNFIG4qaMqUjdD6DUECBAgQIAAAQIE9qqAoNxevfOumwABAgQIECBAgAABAgQIECBAgAABAgQIECDQosBQuRSDb3kAbnVFajmMHRxpryJ1MVakTjapSI3BuOqCsXAt3jaHESBAgAABAgQIENjTAoJye/r2u3gCBAgQIECAAAECBAgQIECAAAECBAgQIECAQGOB8sRQNgluPE6By8NwIyGtT8TpcOUzhhsftMmtC3NFReqxVJMaQ3G1xzQh7sZ5FambdPQyAgQIECBAgAABAgQ2LyAot3krryRAgAABAgQIECBAgAABAgQIECBAgAABAgQI9I1ArSI1VaKOx+lwKQA3loXiRsPEdlWkTsa61BtUpPbNN5ELIUCAAAECBAgQINBDAoJyPXSznCoBAgQIECBAgAABAgQIECBAgAABAgQIECBAYCsCg8OlLAQ3fjiF4fJAXL7eoYrUOP1t5liqSY0BuOJxJltXkbqV++S1BAgQIECAAAECBAhsv4Cg3PYb+wQCBAgQIECAAAECBAgQIECAAAECBAgQIECAwLYJlMdjReqqetSVQNxIpytSYwiuVpWqInXbbqk3JkCAAAECBAgQIEBgGwQE5bYB1VsSIECAAAECBAgQIECAAAECBAgQIECAAAECBDolkCpSR/cPxzDcNlWkTi2E6WMrAbg0HS6bEHd0PlRmFjp1Gd6HAAECBAgQIECAAAECuyogKLer/D6cAAECBAgQIECAAAECBAgQIECAAAECBAgQIBBCrSJ1LKtHHQkTxYS4sfR4oBwGBkstM1UXqyFNf6tVpGYT4VIYLobjpuJXdWGp5fd2IAECBAgQIECAAAECBHpFQFCuV+6U8yRAgAABAgQIECBAgAABAgQIECBAgAABAgR6WqBpRWoMx43sG27r2hZmF8P05HycBDebBeKmVKS25elgAgQIECBAgAABAgT6T0BQrv/uqSsiQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEdkFguSI1mwoXJ8EVVanpcSJ+DZYHWz+rpaUwlypSs0lwKRCX16NOH8vX5+M+CwECBAgQIECAAAECBAg0FxCUa25jDwECBAgQIECAAAECBAgQIECAAAECBAgQIEBglUCjitS8LjUG4w62X5E6GytSs2rUWImqInUVvScECBAgQIAAAQIECBBoS0BQri0+BxMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQL9JjA8PhQmDsXgW5wMN3a4HNdHiulwnatInSkmwk0dn49Vqfl0uJlYnRoHx1kIECBAgAABAgQIECBAYBsEBOW2AdVbEiBAgAABAgQIECBAgAABAgQIECBAgAABAt0rMBB/OjJ6IA/CjReBuPEYiEvBuIkjbVakxsueP1UJUypSu/cbwJkRIECAAAECBAgQILAnBQTl9uRtd9EECBAgQIAAAQIECBAgQIAAAQIECBAgQKC/BVJF6lisQs2CcIfjRLjlqXBxW0cqUiuxGjVOgquvSI3Pp47Nh2ql2t+4ro4AAQIECBAgQIAAAQI9KCAo14M3zSkTIECAAAECBAgQIECAAAECBAgQIECAAAECIdQqUsdSCC5OhpuIgbja+uj+cltEC3OLMQgXa1HXVaTGbZNzKlLb0nUwAQIECBAgQIAAAQIEdl5AUG7nzX0iAQIECBAgQIAAAQIECBAgQIAAAQIECBAgsAmBVJE6sn84TCxPg4uBuKIidTyG4oZGBjfxLs1fUl+RmgJxtbrUtD43tdD8QHsIECBAgAABAgQIECBAoOcEBOV67pY5YQIECBAgQIAAAQIECBAgQIAAAQIECBAg0D8CpViROn4g1qHWBeCyutQYjhuLU+JKg6WWL3ZpsRpmblxbkTofJ8XNqkhtWdWBBAgQIECAAAECBAgQ6E0BQbnevG/OmgABAgQIECBAgAABAgQIECBAgAABAgQI9IzA8NhQVo2apsB1uiJ1cT6vSJ0+NhcDcOkrBuGydRWpPfMN4kQJECBAgAABAgQIECCwAwKCcjuA7CMIECBAgAABAgQIECBAgAABAgQIECBAgEA/C2QVqftiRWoMwo1lNanlvC41rR/pcEXqZKxIjUG4mSIQpyK1n7+zXBsBAgQIECBAgAABAgQ6JyAo1zlL70SAAAECBAgQIECAAAECBAgQIECAAAECBPpWYLsrUmdPVLIAXDYRLk2GO5ZXpKbHxUq1b11dGAECBAgQIECAAAECBAjsjICg3M44+xQCBAgQIECAAAECBAgQIECAAAECBAgQIND1AsOjg6FWj5o/pqrU+HW4HEb3l9s6/+WK1KwetRaEyx9nbpwPS9Wltt7fwQQIECBAgAABAgQIECBAYCMBQbmNdOwjQIAAAQIECBAgQIAAAQIECBAgQIAAAQJ9JNCsInXsUKxKPTwahmJQrp1lfqpSTIKLAbgYiFOR2o6mYwkQIECAAAECBAgQIECgkwKCcp3U9F4ECBAgQIAAAQIECBAgQIAAAQIECBAgQGCXBUpDA2Hs4EiYiOG3NBVuLE6EW1kvh9JgqeUzXFqshqwiNYbgZo7HatRjMQxXPKZ1Fakt0zqQAAECBAgQIECAAAECBLZZQFBum4G9PQECBAgQIECAAAECBAgQIECAAAECBAgQ6LTAuorUg3koLtWkju4fDiGNjmtxUZHaIpzDCBAgQIAAAQIECBAgQKCrBQTluvr2ODkCBAgQIECAAAECBAgQIECAAAECBAgQ2JMC8V/vR/cNhxR8S1PhxtN0uGy9gxWpaRJcnAw3faz2mNelzp1a2JPkLpoAAQIECBAgQIAAAQIE+ltAUK6/76+rI0CAAAECBAgQIECAAAECBAgQIECAAIEuFaivSB07HINwB0fDRHxMVanj8bGtitTqUpi9cT7WoqpI7dLb77QIECBAgAABAgQIECBAYIcFBOV2GNzHESBAgAABAgQIECBAgAABAgQIECBAgMDeEUgVqWNxItxEbSJceswmxHWmInVmMoXh4kS4YykQNxem4uN0fJ62L8WwnIUAAQIECBAgQIAAAQIECBDIBQTlfCcQIECAAAECBAgQIECAAAECBAgQIECAAIFWBZpVpKZA3JHRkIJy7SzzU5Us+LaqIjXVpcYw3NzJSjtv7VgCBAgQIECAAAECBAgQILCnBATl9tTtdrEECBAgQIAAAQIECBAgQIAAAQIECBAgsFWBvCI1Bt+KStSOV6SeSGG4GH7LpsGlxzghrni+MF/d6ul6PQECBAgQIECAAAECBAgQINBAQFCuAYpNBAgQIECAAAECBAgQIECAAAECBAgQILC3BIbi5LcUhJs4XA5jKRAXJ8JNbGdFaqpHLSbDqUjdW99rrpYAAQIECBAgQIAAAQIEdkdAUG533H0qAQIECBAgQIAAAQIECBAgQIAAAQIECOykwEYVqTEQNzw21NbZzE9XYvBtPkxlU+HiRLjadDgVqW25OpgAAQIECBAgQIAAAQIECHRKQFCuU5LehwABAgQIECBAgAABAgQIECBAgAABAgR2VSCrSD0QK1KzSXC1qtR8OlyaFlcaKrV8fmnq22ytIjWrRS3qUdP60bmgIrVlWgcSIECAAAECBAgQIECAAIEdERCU2xFmH0KAAAECBAgQIECAAAECBAgQIECAAAECnRBoVpGa6lLH9g+HMJD+2bu1ZXF+MczcmKbBFSG4OBVuSkVqa5iOIkCAAAECBAgQIECAAAECXSYgKNdlN8TpECBAgAABAgQIECBAgAABAgQIECBAYK8LjOwbjlPhymEiht/SJLixOCFu4lA+Ka5jFakpAFfUo04VE+LmTlb2Or3rJ0CAAAECBAgQIECAAAECfSsgKNe3t9aFESBAgAABAgQIECBAgAABAgQIECBAoDsFSoMDYezgmorUFIqL4biOVKTGwNt0EYJbng6XheFiRepctTtRnBUBAgQIECBAgAABAgQIECCwrQKCctvK680JECBAgAABAgQIECBAgAABAgQIECCwNwWGRkox+JZPhEvht1oILm1ruyK1Ug0zk3NhOk6FWw7ELa/Ph6Xq0t5Ed9UECBAgQIAAAQIECBAgQIBAUwFBuaY0dhAgQIAAAQIECBAgQIAAAQIECBAgQIDARgL1FaljWU1qrEtN4bj41W5FamVmIQvBTalI3egW2EeAAAECBAgQIECAAAECBAhsUkBQbpNQXkaAAAECBAgQIECAAAECBAgQIECAAIG9JlCrSE0huIlDtarUYjrcwZFQGi61ThKnvs2mitRYiTqV1aTOh5lsPU2Jm1WR2rqsIwkQIECAAAECBAgQIECAAIEGAoJyDVBsIkCAAAECBAgQIECAAAECBAgQIECAwF4RWK5IjcG3+nrUVJeaVaSW0j8jt7YsNqtIjYG46WMqUltTdRQBAgQIECBAgAABAgQIECDQioCgXCtqjiFAgAABAgQIECBAgAABAgQIECBAgEAPCWQVqWkiXKpHTdWocb3TFanTsSI1TYfLHrMJcXNh9kSlh5ScKgECBAgQIECAAAECBAgQINDPAoJy/Xx3XRsBAgQIECBAgAABAgQIECBAgAABAntCYCBOfRs7mIJweQAuVaWmQFxWlxrXt6UiNQXjjqpI3RPfYC6SAAECBAgQIECAAAECBAj0gYCgXB/cRJdAgAABAgQIECBAgAABAgQIECBAgED/C2QVqSkAl4Xg6qfDtV+RWo0VqdOT82EmToSbipWo05OzWTVqNiFORWr/f3O5QgIECBAgQIAAAQIECBAgsAcEBOX2wE12iQQIECBAgAABAgQIECBAgAABAgQI9IbAyBlDdfWoaSpcEYiLk+LKE8NtXURlZiGG34pqVBWpbVk6mAABAgQIECBAgAABAgQIEOg9AUG53rtnzpgAAQIECBAgQIAAAQIECBAgQIAAgR4V2KgiNdWlDg6XWr+y6lKYPVkJ+RS4lUDcVKpIjQG5hdnF1t/bkQQIECBAgAABAgQIECBAgACBHhcQlOvxG+j0CRAgQIAAAQIECBAgQIAAAQIECBDoLoFUkTp2ME2DGwkTcRJcehyLjxMxCDd2oBxCKf2zbGvLqorUGIBLVakpBJfCcGm9urjU2hs7igABAgQIECBAgAABAgQIECDQ5wKCcn1+g10eAQIECBAgQIAAAQIECBAgQIAAAQKdF2hYkZqF4zpUkZoF4OIkOBWpnb953pEAAQIECBAgQIAAAQIECBDYkwKCcnvytrtoAgQIECBAgAABAgQIECBAgAABAgQ2EsgqUuP0t/HD8StOglt+zNbbrEhdKipSj9WCcPlUuOmiIrWiInWjW2MfAQIECBAgQIAAAQIECBAgQKAlAUG5ltgcRIAAAQIECBAgQIAAAQIECBAgQIBArwusrUgdSyG4VJGaqlLbrUhdqIaZyRiEq6tFrV9Xkdrr3z3OnwABAgQIECBAgAABAgQIEOg1AUG5XrtjzpcAAQIECBAgQIAAAQIECBAgQIAAgU0LjEwMxWlwMfhWBOCy6XDFhLjyxPCm36fRC9Pkt5kYhEv1qFNFVepMWo/bZk9UGh1iGwECBAgQIECAAAECBAgQIECAwC4JCMrtEryPJUCAAAECBAgQIECAAAECBAgQIECgfYF1FakxEJdXpebT4QbLg61/iIrU1u0cSYAAAQIECBAgQIAAAQIECBDoMgFBuS67IU6HAAECBAgQIECAAAECBAgQIECAAIHVAkPlUjYRbvzQaJwOVwThiglxo7EiNYXlWl2qtYrUNBHueF6VmibETR+L65Nzobqw1OpbO44AAQIECBAgQIAAAQIECBAgQKCLBATluuhmOBUCBAgQIECAAAECBAgQIECAAAECe1UgVaSOFZWoE/Gxfr18xjZVpMZAXFaRKgu3V7/tXDcBAgQIECBAgAABAgQIECCwhwQE5fbQzXapBAgQIECAAAECBAgQIECAAAECBHZLYLkiNVWjHs5rUTtekRonws3E8NvUsbrpcHG9Mru4W5ftcwkQIECAAAECBAgQIECAAAECBLpEQFCuS26E0yBAgAABAgQIECBAgAABAgQIECDQ6wIrFakpCFeE4bJQXJwQd7DNitTFagzBxTrUWkVqVo9aBOLiuorUXv/ucf4ECBAgQIAAAQIECBAgQIAAge0VEJTbXl/vToAAAQIECBAgQIAAAQIECBAgQKCvBLa9IjULwsUA3LEiFBcnwk3FbSpS++rbyMUQIECAAAECBAgQIECAAAECBHZcQFBux8l9IAECBAgQIECAAAECBAgQIECAAIHuFcgrUoeziXBjcRrcRKpKjdPhxuLXxOFyGCwPtn7yS0th9mQlToUrKlJjAG6mCMRNxW2V6YXW39uRBAgQIECAAAECBAgQIECAAAECBDYQEJTbAMcuAgQIECBAgAABAgQIECBAgAABAv0oMDhcCuMx9JbXo9ZXpJZjRepISGG5VpdqqkidTNPg4lecBpdVpWaP+YQ4FamtyjqOAAECBAgQIECAAAECBAgQIECgHQFBuXb0HEuAAAECBAgQIECAAAECBAgQIECgSwXKE0N5EK4+EBfXJ+JkuPIZw22ddWV2MYbhUj1qCsLVAnF5EG7mxvkQltp6ewcTIECAAAECBAgQIECAAAECBAgQ6LiAoFzHSb0hAQIECBAgQIAAAQIECBAgQIAAge0XaFSROpbVpI52pCJ17tTC8jS4KRWp239DfQIBAgQIECBAgAABAgQIECBAgMC2CgjKbSuvNydAgAABAgQIECBAgAABAgQIECDQukBWkZrCb4dTPeqaitQD5TAwWGr5zbOK1Dj9beZYPgluqniciaG4qTgpTkVqy7QOJECAAAECBAgQIECAAAECBAgQ6EIBQbkuvClOiQABAgQIECBAgAABAgQIECBAYO8IrKpIPRjDcFkoLg/HjbRZkboQK1KnVaTunW8mV0qAAAECBAgQIECAAAECBAgQINBUQFCuKY0dBAgQIECAAAECBAgQIECAAAECBNoXSBWpo/uHlwNwE3Ey3NjhGIQ71IGK1Hh6c6cqYTpOgJs+HifDZY9pPX7FCXHz0wvtX4B3IECAAAECBAgQIECAAAECBAgQINAHAoJyfXATXQIBAgQIECBAgAABAgQIECBAgMDuCtQqUseyetSRMJEF4YpA3HZWpMZwXLVS3d2L9+kECBAgQIAAAQIECBAgQIAAAQIEekBAUK4HbpJTJECAAAECBAgQIECAAAECBAgQ2H2B8vhQnAqXJsHFetT01cmK1LlYkZomwh2fDTNxEtxUmghXTIibvXE+LC3t/vU7AwIECBAgQIAAAQIECBAgQIAAAQK9LCAo18t3z7kTIECAAAECBAgQIECAAAECBAh0TGAg/kvZaJz+lgfh4mNdEG4irg+WB9v6rHUVqZN5PWqqSZ2fUpHaFq6DCRAgQIAAAQIECBAgQIAAAQIECJxGQFDuNEB2EyBAgAABAgQIECBAgAABAgQI9I/AdlekpulvtUlw+YS4FIabixPiVKT2z3eRKyFAgAABAgQIECBAgAABAgQIEOhFAUG5XrxrzpkAAQIECBAgQIAAAQIECBAgQKCpwHCsSJ04lE+GG4tVqRPLNakjYWTfcNPjNrNjob4iNYbfUgBuJgbh0lS4mUkVqZsx9BoCBAgQIECAAAECBAgQIECAAAECuyEgKLcb6j6TAAECBAgQIECAAAECBAgQIECgZYGNKlJTberQSHsVqfOnKjEAt1KLmkJw08fSpDgVqS3fNAcSIECAAAECBAgQIECAAAECBAgQ2GUBQbldvgE+ngABAgQIECBAgAABAgQIECBAYL1AabgUxg+mqXDx6/BIfIxf2WM5jMXtpcHS+oM2uWVpsRpmGlWkFoG4xUp1k+/kZQQIECBAgAABAgQIECBAgAABAgQI9IqAoFyv3CnnSYAAAQIECBAgQIAAAQIECBDoM4FGFaljKRAXw3Gj+8ttXe3i/GKYilPgZlL4LX6pSG2L08EECBAgQIAAAQIECBAgQIAAAQIEel5AUK7nb6ELIECAAAECBAgQIECAAAECBAh0p0CqSB3ZPxwmlqfBpalwaUpcPiFuaLRzFakpEFerS03rc1ML3YnirAgQIECAAAECBAgQIECAAAECBAgQ2BUBQbldYfehBAgQIECAAAECBAgQIECAAIH+EGhYkZrqUmMYbiw+tl+RWskmwk0fS5Ph5vP1NCUuTotTkdof30OuggABAgQIECBAgAABAgQIECBAgMBOCAjK7YSyzyBAgAABAgQIECBAgAABAgQI9LDA8NhQVoc6fjivRZ2Ij8sVqfuGQ0ij41pcUkVqFoDLgnBFGK4Ixc3cOB+WqkstvrPDCBAgQIAAAQIECBAgQIAAAQIECBAgsCIgKLdiYY0AAQIECBAgQIAAAQIECBAgsDcF4r8QjRYVqVkALtajZnWpRWWqitS9+W3hqgkQIECAAAECBAgQIECAAAECBAj0k4CgXD/dTddCgAABAgQIECBAgAABAgQIEGgiUBoaCOMH40S4GIJLtai16XCdqkidPVEJU/X1qLEadfr4rIrUJvfDZgIECBAgQIAAAQIECBAgQIAAAQIEdlZAUG5nvX0aAQIECBAgQIAAAQIECBAgQGDbBJYrUg/FMFxWk5qqUvNw3Oh2VKQej1WpMRCnInXbbqk3JkCAAAECBAgQIECAAAECBAgQIECgQwKCch2C9DYECBAgQIAAAQIECBAgQIAAgW0XSBWpMfA2EUNwaytSx+K24dHBtk5hfqqSBd+mUwAufqUJcTPH42S4+Dg3tdDWezuYAAECBAgQIECAAAECBAgQIECAAAECuykgKLeb+j6bAAECBAgQIECAAAECBAgQILBGIFWkjsWK1IliKlwKxK2sl0NpsLTmiM0/Xaouhdkb55crUmdSGK4IwqUw3GKluvk380oCBAgQIECAAAECBAgQIECAAAECBAj0kICgXA/dLKdKgAABAgQIECBAgAABAgQI9IdAmvyWV6NuY0VqMRUuVaNmE+JUpPbHN4+rIECAAAECBAgQIECAAAECBAgQIECgJQFBuZbYHESAAAECBAgQIECAAAECBAgQ2ECgqEgdj9PglgNx2XoKxo12riJ1MlakLgfh8rrUuVMqUje4M3YRIECAAAECBAgQIECAAAECBAgQILBHBQTl9uiNd9kECBAgQIAAAQIECBAgQIBAewLNKlJTVer44Q5VpMapcDNFNWqtIjXVpS7Mq0ht7+45mgABAgQIECBAgAABAgQIECBAgACBvSYgKLfX7rjrJUCAAAECBAgQIECAAAECBDYtkCpSxw6PhIlDcRJcCsClx/g8rY/uHw5hIP3TSmvL4vximJmcD/UBuKljaSrcfLZ9qbrU2hs7igABAgQIECBAgAABAgQIECBAgAABAgTWCQjKrSOxgQABAgQIECBAgAABAgQIENgzAvFfRkb3DechuCwAtzoQNzw21BbF/FQlC75NxylwKlLbonQwAQIECBAgQIAAAQIECBAgQIAAAQIE2hIQlGuLz8EECBAgQIAAAQIECBAgQIBAtwvkFalFAC5Woo4fHA0T8TGrSI0T4kpDpZYvIU19mz2RwnApCJemwdUF4lJF6pyK1JZxHUiAAAECBAgQIECAAAECBAgQIECAAIEOCgjKdRDTWxEgQIAAAQIECBAgQIAAAQK7I1BfkVoLwE2oSN2dm+FTCRAgQIAAAQIECBAgQIAAAQIECBAg0IUCgnJdeFOcEgECBAgQIECAAAECBAgQILBeYHT/NlakTlfCzPH5MJVNhZtfmQ4Xt82drKw/GVsIECBAgAABAgQIECBAgAABAgQIECBAoKcEBOV66nY5WQIECBAgQIAAAQIECBAg0L8CpcGBWIdaVKTWHrOpcPm2dipSQ6xInWlUkTqZV6aqSO3f7ytXRoAAAQIECBAgQIAAAQIECBAgQIAAgSQgKOf7gAABAgQIECBAgAABAgQIENgxgaHRwTB+aCRMHC7HUNxIXC/H9fQ4Ekb3DYdQSv9U0dqyWKmGmSz4FifCHc8DcFNxItxMWo+PSzEsZyFAgAABAgQIECBAgAABAgQIECBAgACBvSkgKLc377urJkCAAAECBAgQIECAAAEC2yYwEgNv4zEIN5EF4UbCWAzCTaQJcfFxeGyorc+dX1ORmkJwU1koTkVqW7AOJkCAAAECBAgQIECAAAECBAgQIECAQJ8LCMr1+Q12eQQIECBAgAABAgQIECBAoNMCWUXqwTz4libCpWlwKQSXrR8cCaXhUusfmSpST1bC9LE0BS5NhSumw2Xrc0FFauu0jiRAgAABAgQIECBAgAABAgQIECBAgMBeFhCU28t337UTIECAAAECBAgQIECAAIEmAnlFahGCy4JwK4G4sTYrUquxInW6viI1heBiNWoejlOR2uSW2EyAAAECBAgQIECAAAECBAgQIECAAAECbQgIyrWB51ACBAgQIECAAAECBAgQINDLAs0qUsfilLjy+HBbl1aZWciCb1MxADdTTIdTkdoWqYMJECBAgAABAgQIECBAgAABAgQIECBAoA0BQbk28BxKgAABAgQIECBAgAABAgS6WWCgNJDVoY7FiXATqSI1q0dNNalxvUMVqTNxGtxUFoSLgbhsPU2Gm1WR2s3fGM6NAAECBAgQIECAAAECBAgQIECAAAECe1BAUG4P3nSXTIAAAQIECBAgQIAAAQL9IzA0UloJwNVXpMb1sf1xKlwMy7W6LFekLteiqkht1dJxBAgQIECAAAECBAgQIECAAAECBAgQILC7AoJyu+vv0wkQIECAAAECBAgQIECAwGkFsorUNBEuC8LFiXBxfSJOh+tkRep0CsPFiXDZY1GVOnuictpz8wICBAgQIECAAAECBAgQIECAAAECBAgQINALAoJyvXCXnCMBAgQIECBAgAABAgQI9LXA2orUVJWaalJTXWpaHxwutX791aUwe7KSheDWVaTGYNzC7GLr7+1IAgQIECBAgAABAgQIECBAgAABAgQIECDQIwKCcj1yo5wmAQIECBAgQIAAAQIECPS2QFaRmgJwWQiufjpcpypS58NMDL5NHYuT4SZnw3R6jM9n4qS46uJSb+M5ewIECBAgQIAAAQIECBAgQIAAAQIECBAg0KaAoFybgA4nQIAAAQIECBAgQIAAAQI1gVUVqfWBuDgZrjwxXHtZS4+VmYUYfiuqUVWktmToIAIECBAgQIAAAQIECBAgQIAAAQIECBDYuwKCcnv33rtyAgQIECBAgAABAgQIENiiQKpIHTuYpsGVw0SsRu1oRepSrEg9kVek1gfipuJEuJkYkKuoSN3i3fJyAgQIECBAgAABAgQIECBAgAABAgQIECCwIiAot2JhjQABAgQIECBAgAABAgQIhFSROnYwVqTGINxEDMSlx7EUjIsT4kYPlEMKy7W6VBeqsQ51dUXqTKxIzcJwcUqcitRWZR1HgAABAgQIECBAgAABAgQIECBAgAABAgQ2FhCU29jHXgIECBAgQIAAAQIECBDoQ4GRM4biVLg8DJc/pilx8XmnKlJTNWoMwE2vrUg9WQlhqQ9BXRIBAgQIECBAgAABAgQIECBAgAABAgQIEOhyAUG5Lr9BTo8AAQIECBAgQIAAAQIEti6QVaTG6W/jh4sAXO2xCMMNlge3/qa1I9ZWpE7GQFysRk2T4tKjitQalEcCBAgQIECAAAECBAgQIECAAAECBAgQINA9AoJy3XMvnAkBAgQIECBAgAABAgQIbEFgqBwrUoupcNtRkTpTBOBqtagpBJetT8aK1AVj4bZwq7yUAAECBAgQIECAAAECBAgQIECAAAECBAjsuoCg3K7fAidAgAABAgQIECBAgAABAs0ERiZiRerhkRiIK4eJ+JjXo6bK1HIoTww3O2xT2yszC1k16kycBDdVVKXOxMepGIibVZG6KUMvIkCAAAECBAgQIECAAAECBAgQIECAAAECvSIgKNcrd8p5EiBAgAABAgQIECBAoA8Ftr0iNQbepo/FStQUhMvCcCpS+/DbyCURIECAAAECBAgQIECAAAECBAgQIECAAIHTCgjKnZbICwgQIECAAAECBAgQIECgHYG8IrUcp8GNZpPg8qlw+YS40QPlkMJyrS7VxWpIE+HyIFx8jNPg8kBcXFeR2iqr4wgQIECAAAECBAgQIECAAAECBAgQIECAQN8JCMr13S11QQQIECBAgAABAgQIENh5gbUVqWMH83rUiUMjoXxGmxWps4thpgjAqUjd+XvrEwkQIECAAAECBAgQIECAAAECBAgQIECAQD8ICMr1w110DQQIECBAgEDHBMoTQ+Gm5x4IB24xHs44MhpG9g2H4dHBMDA4EJYWl0IlhjXmYo3fqaOz4cZ/mw7/+1s3hvmphY59vjciQIDA6QR2679TyxWph+JkuMMxBJceYwiutj5YHjzdqTffv7QUZlNFapwMNxPrUaeyUFxtOtx8qMz472xzPHsIECBAgAABAgQIECBAgAABAgQIECBAgACBzQgIym1GyWsIECBAgACBvhcYO1gOt3vAzbKQXCmG4ja7VGN4LoXlvvPpa8PM5PxmD/M6AgQIbFlgt/879eBL7hxG95e3fN61A1Sk1iQ8EiBAgAABAgQIECBAgAABAgQIECBAgAABArshICi3G+o+kwABAgQIEOgqgbPvdVa43YNuFrYSkFt7AdWFpfCdT10bfvCF69fu8pwAAQJtC3TDf6fu89Tbh0O3OmPDa1mIUzen40S47OtYnAiX1uN0uFSXOnuiEsLShofbSYAAAQIECBAgQIAAAQIECBAgQIAAAQIECBDYNgFBuW2j9cYECBAgQIDA/8/efYBHUa1/HH8JIYD0QKQLSK8qRUWaKCB28SrYEBuCVJGLFcQCImADBEHEwrUgdkVEAUFFrwXpvSjSQXpvSf55x3+4mZkzO7vJbrIL3/M8MXvKnJn5DEx4nvw8J9oFdBvBOleVl9K1i4btUrcs3SNLpmyQ1BTSIGFDZSIETmOBaHpP1buugpStV8y4Raq1XWratqnHD7FF6mn8x5VbRwABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgqgUIykX14+HiEEAAAQQQQCCSAnWvOSusIbn0a9Ww3OLP16dX+Y4AAghkWiCa3lN58sdL8vFk0RU0KQgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBArAkQlIu1J8b1IoAAAggggEBYBHQbw2qXlg7LXKZJVs3cwjasJhjaEEAgaAHeU0FTMRABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQ8BUgKOdLxAAEEEAAAQQQONUE8hdNkCb3Vpe43PpPocgUXXHpx/Er5fCeY5E5AbMigMApLcB76pR+vNwcAggggAACCCCAAAIIIIAAAggggAACCCCAAAII5IAAQbkcQOeUCCCAAAIIIJCzApHaytB5V2zB6hShjgACwQrwngpWinEIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAQHACBOWCc2IUAggggAACCJwiAgkF4qV595oRXU0unSolOVW+H71cjh08kd7EdwQQQMBXgPeULxEDEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIGQBQjKhUzGAQgggAACCCAQywJlz02U2peXy7ZbWPrVRtm0YFe2nY8TIYBA7Avwnor9Z8gdIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAQPQJEJSLvmfCFSGAAAIIIIBABAVqXVFOyp2TGMEz2KfeuHCXLJu60d5IDQEEEAggwHsqAA5dCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggEAmBQjKZRKOwxBAAAEEEEAgNgUa3nq2JJ5VMNsuftf6AzL3nT+y7XycCAEEYl+A91TsP0PuAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBKJPgKBc9D0TrggBBBBA4BQSuPXWWyUhIcF2R7/99pssWbLE1hbJSnx8vPTo0SPkUyxcuFBmzZoV8nHBHpCUlCTqE2qZPXu2LFiwINTDTo6/qHN1KVgi78m680PZ4mfJ+TWaO5t96z8unSnb92xxjTuw46j8NH6lq50GBBBAwEvA9J5qXPNiKZUY2rbRx44fkS9//dDrNCfbeU+dpOADAggggAACCCCAAAIIIIAAAggggAACCCCAAAIInMICBOVO4YfLrSGAAAII5KxAhw4dZNKkSa6LeOKJJ+TJJ590tUeq4YwzzpCDBw+GPP3o0aMzFbAL9kTnnnuuzJ8/P9jhJ8f17t1bRo4cebIe6oeLe9aShILxnoc1r9tGHrt5uGe/V8djb3aTuat+dHUfO3BCZo9a5mqnAQEEEPASML2nnuw4Ui6s2cLrEGP73oN7pP1g/2N4Txn5aEQAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIFTTICg3Cn2QLkdBBBAAIHoEMiXL58sX75cKlasaLugffv2SaVKlWTXrl229khWCMrZdS/pW0fiE+LsjRlq4Q7KnTiWIt8+n30rCGa4FT4igECMCpjeU5EMyvGeitE/KFw2AggggAACCCCAAAIIIIAAAggggAACCCCAAAIIhCRAUC4kLgYjgAACCCAQnMDDDz8sQ4YMcQ3WleR0RbnsLATl7Nqt+tWVuHj9J5C5hDsol3IiVWYMX2w+Ga0IIICAQcD0nopkUI73lOEh0IQAAggggAACCCCAAAIIIIAAAggggAACCCCAAAKnnABBuVPukXJDCCCAAAI5LXDmmWfKmjVrpFChQrZL2b17t7Wa3N69e23tka5kNij38ssvS8+ePSN2eZnderVXr14yatSoTF9X64fqSS7vBeUks0G5R9+4T35f/ZPrulJTRKYPXeRqp+H0EihatKi1wmRiYqLs37/fWlVSV5bU9wIlNgT0nV66dGkpWbKkxMXFyZ49e6znt2PHDjl06FBYb8L0nopkUI73VFgfH5MhgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBAlAoQlIvSB8NlIYAAAgjErsC4cePk3nvvdd1A//79ZfDgwa72SDcECsrNnj1bfv/9d+MlvPLKK7J27VpjXzgaAwXlJkyYYIVQnOdJTk6WoUOHZmnr2tYPpwXlvBeUCxiU+2jOROclWfVDRw/KpNkT5ETycVd/ampaUO7ZrAflChcuLI0bN067dvfFHz16VGbNmuU6Nw3ZLxAfHy+XXXaZXHLJJVYwVrdarlixomhQzlQ2b94sP/74o3z//fcyadIk0dAVJecE9O9XjRo1pGHDhtKgQQPR91T58uWlVKlSou9SU0lN+0u+bNky+e9//yszZsyQTz75RI4dO2YaGnSb6T11WYPrpELJysY5yhavIBfWbOHq23twj7Qf7G53DgzXe8o5L3UEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAIJoECMpF09PgWhBAAAEEYl6gbt26Mn/+fMmdO7ftXnbu3GmFZnQlqewugYJy559/vvz222/ZfUnW+QIF5TRUFKmV99o8Ui/g/XqtKLfnwC7p8EzLgMd6dX4zJPNBuaSkJOndu7d0797dM2y1bds2K8jjdX7aIy9Qv359uf322+Xmm28WXVUyM+Xw4cPy9ttvW2HQSIZUM3Ntp/Ix+r7u0aOHNG/e3PoqUaJElm5X/z6OHz9ehg0bZq0emJnJ/N5Tzjmb1Wkt/W95ztkswQbl9MCsvKdcJ6YBAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEIhCAYJyUfhQuCQE/AR027aPPvrIOKxjx46yceNGYx+NCCAQeYGvv/5a2rRp4zrRQw89ZIUmXB3Z0EBQzo7sF0CJlqCcrmLVt29f6dy5s+dKVul3RlAuXSJ7v+vfLd2eWANytWrVCtvJdRvPPn36yKuvvhq2OZnIW6BgwYKZDrR5zyqybt06ueuuuzK12qPfe8p5XoJyThHqCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggIBbgKCc24QWBKJeoHTp0qJbtZlK9erVZdWqVaYu2hBAIMIC9erVk4ULF7rOcuDAAWu1r4MHD7r6sqOBoJxd2S+AktNBuWrVqokGKzX4nCdPHvvFe9QIynnARLi5Zs2a1pabkTrNu+++a/05SElJidQpmDdNIFJBOcXVbVm7du0acujR7z3lfHAE5Zwi1BFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABtwBBObcJLQhEvQBBuah/RFzgaSowZswYue+++1x3P3HiROnUqZOrPbsaCMrZpf0CKDkVlNOtaB955BG54YYbJC4uzn7RPjWCcj5AEeqOdFBOL3vChAlyzz33ROgOmFYFIhmU0/k1LHfHHXeI/iwItvi9p5zzEJRzilBHAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBNwCBOXcJrQgEPUCBOWi/hFxgaehgAYtdKXHQoUKue6+devWMmPGDFd7djUQlLNL+wVQsjso17RpUysgd8UVV9gvNIQaQbkQsMI4NNig3N9//y0rVqwQfU66rWrFihVFV4AtWbJkUFfzwAMPyIsvvhjUWAaFLhBMUO7IkSOyadMma3t7/R4fH289x0qVKklSUpLvSY8ePSp16tSRNWvW+I7VAX7vKeckBOWcItQRQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAbcAQTm3CS0IRL0AQbmof0Rc4Gko0KVLFxk7dqzrzjU8V758ecnJrRMJytkfi18AJTuDch06dJBJkybZLzATNYJymUALwyGBgnKzZ8+Wjz/+WGbNmiVLly61VhVznlIDVo8//rh069Yt4CqC+/btE92SV58zJfwCpqDcokWL5IsvvpCpU6fKypUrZefOnZ4n1rDcwIED5bbbbpPcuXN7jtO5rrzySs/+jB1+76mMY/UzQTmnCHUEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAwC1AUM5tQgsCUS9AUC7qHxEXeBoKLFiwQM455xzXnT/33HPSr18/V3t2NhCUs2v7BVCyMyin2zG+8cYb9gv0qP3222/SqFEjYy9BOSNLxBudQbndu3dbz/PVV1+1wlXBXkD9+vXlzTfflLp163oeMmrUKOnVq5dnPx2ZF8iTJ4/MmzfPWhVUw3H69ddff4U8Ya1ateSzzz6TKlWqeB6rq8ppcNKv+L2nnMcTlHOKUEcAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEE3AIE5dwmtCAQ9QIE5aL+EXGBp5nAhRdeKP/973+Nd63hOV2ZKCcLQTm7vl8AJdqCct9//70MGjRIpk+fLkuWLJHatWvbbyitRlDORZItDelBucOHD8uIESNk6NChsmfPnkydW8NV+q7Inz+/8fgtW7ZI2bJljSvTGQ+gMUcEGjZsKD/99JNo+M5UBgwYYP19NvVlbPN7T2Ucq58JyjlFqCOAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACbgGCcm4TWhCIegGCclH/iLjA00xAV4Lq1KmT66419GJaZc41MMINBOXswH4BlGgJyk2bNk0GDx4sc+bMOXkDBOVOUkTFBw1D9e7dW959911rNbKsXlTfvn1FV6H0KhrK/eWXX7y6aY8SgUceeUSeeeYZ49Xo3+dmzZoZ+zI2+r2nMo7VzwTlnCLUEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAG3AEE5twktCES9AEG5qH9EXOBpJJArVy7Zvn27lChRwnXXTz31lAwcONDVnt0NBOXs4n4BlJwMyqWmpsqnn35qBeR+//13+4Wn1QjKuUhOqYbcuXPLhg0bRH/Om0rXrl1l3Lhxpi7aokigatWqsmrVKuMVrVu3TipVqmTsy9jo957KOFY/E5RzilBHAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBNwCBOXcJrQgEPUC0R6UK1asmLRt21YaNGggZ555ppQsWdIKER07dswKFOkWgfoL5ClTpsiKFStyxLtly5bSvHlzOeuss6R8+fKSlJQk+/bts7YvXLNmjXVtP//8s6SkpET0+nSrPf2FeoUKFayvihUrWteUN29ey0q32tu6davo982bN8sPP/wgf//9d0SvSSevV6+eXH755dYv8/UZ6tf+/fslISHBui4Nhs2bN0+++uorqx7xCwrhBLpt4dVXX225ahhBv/TP5KZNm+SPP/6QtWvXyrJly2TmzJlh2cJQt8HU8JKpXHHFFZaRqS872wjK2bX9Aig5EZRLTk6WSZMmyZAhQ2Tp0qX2C85QIyiXAeMU/agrCV522WXGu3viiSfkySefNPbRGD0CcXFx1s9Mffc6y5EjRzy318041u89lXGsfiYo5xShjgACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgi4BQjKuU1oQSBHBdq0aSNTp071vQZddcZUNGwRSjl+/HhQv7D1m1NX1brlllukS5cuctFFF4nX9Tnn0VDaSy+9JGPHjpVQrz3jXLrCzt13352xyfr8zjvvnNwSU0Ne3bt3F12Rp1q1aq6xzgYNVemWehroC3dp0qSJtdJY69atQ5pag3sa4Pv888+tr+XLl4d0fKDBhQoVkoceekhuv/12KzwYaGx6n65+9euvv8rzzz8vH3zwQXpzWL9rUNC0Wptep253mF407KjPV79M49PHpX/XkOawYcPk7bffFv17kNnSrVs3GT16tPHw4sWLy65du4x92dlIUM6u7RdAyc6gnIZkH3jgARk5cqQV4rRfqbtGUM5tcqq1vPDCC9KnTx/jbY0ZM8Z6xxk7aYwqAf0ZU716ddc16c/N+Ph43yC+33vKOTFBOacIdQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEDALUBQzm1CCwI5KqCryOhqMtlVTpw4IXny5MnS6Vq0aGEFpXQFucwWXUFJA2xz5szJ1BSvvfaaMSinIaiOHTtaq9p9+OGH0rRp05DnHzFihNx///0hH2c6QANyuhrQpZdeauoOuW3y5MnSoUOHkI9zHtCpUyd59tlnpVSpUs6uoOvff/+99OzZUxYtWhT0McEM1BX0TMG32267TTQIqaVdu3ZW4M20eo/fOTZu3GitOqd/FzJT9BnceOONrkNXr14dVCDTdWAEGgjK2VH9AijZGZSzX5l/jaCcv1Gsj9CAtgbITeXll1+23rOmPtqiR0D/ZwFdpdb0M2n9+vXWCrJ+V+v3nnIeT1DOKUIdAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEHALEJRzm9CCQI4KxFpQ7pFHHpFnnnkmLGa6NetNN90kn3zyScjzBQrKDRo0SKZPnx70Kmmmk+scAwYMMHUF3abP9ssvvwx6tb1gJtbtQ1u1ahXMUM8xGpDTleTCUQ4cOCBXXXWVfPfdd+GYzprDLyh3xx13iD7/YFcxNF2YbnWrf/4yU3TFO91e2FnSQ5rO9pyoE5Szq/sFUAjK2b2oZa/AwIEDRbdYNZWHH35Yhg4dauqiLYoE6tSpI4sXLzZekf57RFcP9it+7ynn8QTlnCLUEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAG3AEE5twktCOSoQKwE5eLi4kRXWuvRo0dYvXT7VV0pbNKkSSHN6xWUe//996VKlSqSldXu9EJ0y1OdY8GCBSFdV/pg/aX5jz/+KIULF05vCsv3rAbldNUiXb0onOXw4cNyzTXXyIwZM8IybaCgnG6nO2HCBNGtf7NSMhuUq1Gjhnhtf6t/N7y2ZM3KtWbmWIJydjW/AApBObsXtewVmDhxorUSqumsusX5e++9Z+qiLYoEnnvuOenbt6/xioJdFdDvPeWcnKCcU4Q6AggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIOAWICjnNqEFgRwVyO6g3Pz586V+/foh3/OwYcOkX79+vsfpdpYrV66UtWvXWtt61q5dWwoUKBDwuEOHDlmhtBUrVgQcl7HTKyiXcYzzs55Hr6t06dLGrT2d47/99ttMbZmqocJVq1ZJ5cqVnVPa6notf/75p+gKZfv375cyZcpYIT89Ll++fLax6ZWsBOXat28vGiT0K/oc9Pp1tbikpCTrz0vx4sUDHrZz506pWbOmaMgtq8UrKNerVy9rG9tixYpl9RSS2aBc586d5dVXXzWev1GjRjJ37lxjX3Y3EpSzi/sFUAjK2b2oZZ+AboW+ZcsW8XrH6s/QZcuWZd8FcaaQBfx+trZs2VJmz57tO6/fe8o5AUE5pwh1BBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQMAtQFDObUILAjku4Lc6lga7Nm3aZLxOXeFKQ03BltTU1GCHnhynW4ZNmzYt4CpeU6ZMEd0+bunSpXL06NGTx2porFKlSlbIrkuXLifbnR80wHfhhRcGvR1msEG5ffv2iW5d98MPP1grgekKdlo0lNahQwdrSzsNKniVihUryl9//eXVbWxv1qyZfP/998Y+9X/jjTessNUvv/xiHKN/HsqVKyeNGze2VvDT+dJLZoNyiYmJVtjCtGVo+tyTJ0+WwYMHy6JFi9KbrO/q07ZtWxk+fLhUr17d1pexoqse6epHWS1eQbnt27fLmWee6Zpen89nn31m/RnV8KGG9vR+9Vr174eu7qdBhvz58588NrNBOfV59NFHT86T8YMGQjWMGQ2FoJz9KfgFUAjK2b2oZZ/AFVdcYW3RbTrjkiVLpG7duqYu2qJAQP99c8MNN8jrr7/u+T8EfPHFF9aKq8Fcrt97yjkHQTmnCHUEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAwC1AUM5tQgsCUS+gQbnNmzcbr1PDQKEE5YyTBGgsUaKE6C/rvQJWe/fulfvvv1/efPPNALP803XdddeJBty8Vs7p2bOn6BZlwZRggnKrV6+2fkEdaKU6DaN99NFH1ipzpvM++OCDVkDM1OfVplvU6upnpqKr8ukWbaEUXQFQt0u96aab5JtvvpGrr746lMOtsSNHjhT1NRUN791zzz3WL/tN/eltGjTT56yhM69y8cUXy3fffefVHVS7V1DOefCuXbvkiSeekLFjx8rx48ed3ba6Bg815NaxY0cr8JnZoJyuJqeryjnLkSNHbEE8Z3921wnK2cX9AigE5exe1LJHQEPRukW3/hwylccee0yeeeYZUxdtOShQqFAh6+ewPp9atWp5XomusKtBx0D/Bsl4sN97KuNY/UxQzilCHQEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBwCxCUc5vQgkDUC+RkUG7QoEGivww2FQ006XaToay4Vq1aNVm4cKFxa9H169dbW4/6hZ70WvyCcgsWLBDd7mzPnj2mS7e1aXhq4sSJtrb0ytSpU+XKK69Mrwb1Xe+jfPnyrrGZmSvjJBpW1BVsdJu+UIoG3PSYIkWKGA/TFdKGDBli7HM2asBMV7Vr0qSJs8uqh2NVuWCCcmrcunXrkEOiGjq84447rHBnSkqK8R4CNX766ady7bXXuoZokLVs2bKu9pxqIChnl/cLoBCUs3tRyx6B22+/Xd566y3jyQ4ePChVq1YN+X1vnIzGkAR0lbh27drZjtF3aoUKFawvXbHUr+hqthoq//rrr/2Gnuz3e0+dHPj/HwjKOUWoI4AAAggggAACCCCAAAIIIIAAAggggAACCCCAAAJuAYJybhNaEIh6gZwKyukvhjWQ5LUC3DXXXCO6rVioRYN3GsAzFQ2tvf3226YuWyZoA2AAAEAASURBVJtfUE7DTJ9//rntGK9KQkKCFfYrVaqUa4jev/5yPNiSO3dua3Uz03a6mVmdLtjzBhp36623eppqaPG8886TULbk1bDjsmXLRO/VWXRlNf3zGkxA0Xlset0vKHf48GFrFZ9169alH5Jt33X1p4suush1vmjbIpGgnP0R+QVQCMrZvahFXqBYsWLWe9T0c0fP3r9/f2sVzMhfCWdwCjz11FMyYMAAZ3PQdf2fB3Tl18WLFwd9jA70e085JyMo5xShjgACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgi4BQjKuU1oQSDqBXIqKHfffffJmDFjjD6vvPKKdOvWzdjn16jBNF3xrWbNmq6hH374odx4442udmdDoKCchrjq1KkTUvgr0C/GCxcuLPv373degrGu4QfdEtRUdPWg//znP6auiLZ99dVX0rZtW+M5QgkUZpxAt2Dt1KlTxqaTn3VrUn0+mS1+Qbnhw4eLhg5zoug2x7rKk7PodrO67Wy0FIJy9ifhF0AhKGf3ohZZAQ1Sf/bZZ57baP/5559WGFiDx5TsFwj074FAVzNnzhzr30y6nfuxY8cCDTX2+b2nnAcRlHOKUEcAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEE3AIE5dwmtCAQ9QI5FZTTLcPatGnj8jlx4oS1ypxuLZbZ0r17d3n55Zddh+tKZCVKlJDk5GRXX8aGQEE53VrTazu7jHNk/KzhvMmTJ2dsOvlZg1Fr1qw5WQ/0QUOAhw4dMq62pr98HzhwYKDDI9KnwT0N8DnLzp07Rbdz9bN2Hqd13db222+/NXXJhAkT5J577jH2BdMYKCingcVKlSqJXntOlN27d0vRokVdp/7kk0/k+uuvd7XnVANBObu8XwCFoJzdi1pkBTToO3ToUONJdHXPK664QqZNm2bspzHyApkNyum/azQMH2yw3nknfu8p53iCck4R6ggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIICAW4CgnNuEFgSiXiAngnK6raaGggoVKuTy+emnn6RJkyau9lAaateuLbpdpak0aNBA5s2bZ+o62RYoKFeuXDnZtGnTybHBfND70dVgTEW3JtUV8IItK1euFN2e1Fm2bNki5557rmzfvt3ZFbG6hsr++OMP4/z6C31d5S4zJT4+XjTQZgqNqZWaZbYECso9/fTT8vjjj2d26iwdp38nNCRqKm+88Ybcddddpq4caSMoZ2f3C6AQlLN7UYucQNOmTWXWrFmi71BTyalAtelaTte2zAbl1EtDcvrz4JFHHrFC86EY+r2nnHMRlHOKUEcAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEE3AIE5dwmtCAQ9QI5EZSrX7++/P7770Yb3WYy48pouo2clvTvXp8z9sfFxYluCarfneVf//qXfPzxx85mW90rKKdBprx580pKSoptvF8lUKCsefPm8sMPP/hNcbL/008/Fd3S1FQ0ZKhblga7Qp1pjlDa1FK3szWVrG6ROnXqVLn88stdUx8/flwKFiyYqa3ndLJAQbnExEQrwOk6aTY06J9VvTfTn1ldwVBXMoyWQlDO/iT8AigE5exe1CIjULlyZdGfAWeeeabxBLqKq64mF+rPL+NkNGZaICtBufST/vLLL3LVVVfJjh070pt8v/u9p5wTEJRzilBHAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBNwCBOXcJrQgEPUCORGUu/fee2XcuHE5YnP//ffLiBEjAp7bKyj3119/ScWKFQMea+rMly+fHD582NQlrVu3lhkzZhj7TI0DBgwQ/UW7V9GtTnW+Dz74QL755hvZsGGD19Astz/00EPy7LPPGucJNQDonOSFF16QPn36OJutuq6ot3r1amOfX6NXUE7bvQImfnOGq19XA0xKSnJN98UXX8g111zjas+pBoJydnm/AApBObsXtfAL6JbiGpLTrbxNZd26ddKwYcMc21badE2na5tuoa5fGUuBAgVEg45VqlSxvs4//3y57LLLMg5xfdafgRomX7t2ravP1OD3nnIeQ1DOKUIdAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEHALEJRzm9CCQNQL5ERQ7tFHH5XBgwfniI0Gu3TbskDFKyinK79pACzUoquEaYDNVEINyul2tcuWLRPdAjaYoluj/vrrr9aXbjmrW5fu3bs3mEN9xzzzzDOeliVLlszSNrBdunSRsWPHGq+hUaNGMnfuXGOfX6NXUE6NLrjgAr/DI9q/dOlSqVWrlusc4diO2DVpFhoIytnx/AIoBOXsXtTCK6Ahq5kzZ3q+v3Sbc93+e/ny5eE9MbNFVKBZs2YyfPhwz+eqJ9ef7zVq1LBWI/W7GL/3lPN4gnJOEeoIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAgFuAoJzbhBYEol4gJ4Jyzz33nPTt2zdHbIYNGya6Elqg4hWUe//99+Wmm24KdKixL5xBOT2Bbr2qW7BmtmjI7KOPPrK+Mrsym5775Zdflu7duxsvIz4+3jMcaDzA0XjDDTdYq+I5mq1qq1atrGCIqc+vzSsoN3nyZOnQoYPf4RHtnz17trRo0cJ1jhUrVkjNmjVd7TnVQFDOLu8XQCEoZ/eiFj6BPHnyyOeffy5t27Y1TnrkyBFr1dI5c+YY+2mMfoF77rlHxo8f73mh+jN4zJgxnv3pHX7vqfRx6d8JyqVL8B0BBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQ8BYgKOdtQw8CUSuQE0G5119/Xe68884cMclKUO7tt9+Wjh07hnzdgYJybdq0kenTp4c852OPPSZPPPGEaCAtK0VDc/oslixZEvI0//nPf+S2225zHafhjPz587vaQ2nQ4MdXX31lPOT666+XTz75xNjn1+gVlAvmz4Xf3Fnt1+1yNSDoLLolq67QFy2FoJz9SfgFUAjK2b2ohUdAf67oz6Sbb77ZOKGuYnrjjTdm+l1pnJTGHBF46aWXpHfv3sZzb9myxdq21Wt79/SD/N5T6ePSvxOUS5fgOwIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCDgLUBQztuGHgSiViAngnJegSBFmj9/fti2BnWip6amim77+vPPPzu7bHWvFeWiKSinF6xbhWpYrWrVqrbrD7Wiv2Dv1auX6H2HUnRVOg2tOcuBAwdEt4jNStFV47wChBpW1GeRmeIVlOvWrZu88sormZkybMfoqkD33Xefa74TJ06IrhwVLYWgnP1J+AVQCMrZvaiFR2DUqFHSo0cPz8k6d+4c8jvdczI6clQgISFBfvzxR2nYsKHxOnr27Gmt8Grs/P9Gv/eU81iCck4R6ggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIICAW4CgnNuEFgSiXiAngnJvvvmmdOrUyWij17N161ZjX3Y1xkpQTj1y584tuiqdrux23XXXiYaYMlsefvhhGTp0aNCHa1jt1ltvNY7Ply+fHD161NgXTKNucfvee+8Zh+qqaxrSy0zxCsqp3zvvvJOZKcN2zJNPPimPP/64cb7ExETZvXu3sS+7GwnK2cX9AigE5exe1LIuMHDgQGtFUa+ZNBA+ZMgQr27aY1BAf85//fXXxit/66235I477jD2pTf6vafSx6V/JyiXLsF3BBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQMBbgKCctw09CEStQE4E5V544QXp06eP0aRChQqyfv16Y192NcZSUC6jScGCBaV58+bWSnONGjUS/SpRokTGIQE/Hzp0SGrUqCEbNmwIOC69c/To0aIrsZlKxYoV5a+//jJ1BdWmfz70z4mptG7dWmbMmGHq8m2L5qDcLbfc4hnWu+SSS2TWrFm+95cdAwjK2ZX9AigE5exe1LImoKt/jhgxwnMSfW/27dvXs5+O2BTQn+X688tUdLW5pk2bmrpOtvm9p04O/P8PBOWcItQRQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAbcAQTm3CS0IRL1ATgTl+vfvL08//bTRRoNaK1euNPZlV2OsBuWcPrly5ZJzzjnHWnHusssukxYtWlgr0DnHZaxPnjxZOnTokLHJ87OuWKSr0JlK48aNfbe4NR2X3qYr2z344IPpVdv3888/X3777TdbW7CVaA7KlS1bVjZu3Gi8lWhaIYqgnP0R+QVQCMrZvahlXuD2228XXZFV3+2m8sYbb8jdd98tus045dQT0P+JoHz58q4b2759u5QsWdLVnrHB7z2Vcax+JijnFKGOAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCLgFCMq5TWhBIOoFciIod99998mYMWOMNldffbVMmTLF2JddjadKUM7ppSFE3d7zxhtv9AxapKSkSIECBeTIkSPOw111Dcl5be/Xrl07+fTTT13HBNswceJE6dixo3F4VsKU0RyU05tds2aNVK5c2XXfn332mbW1rqsjBxoIytnR/QIoBOXsXtQyJ6Bba3/44YeeYWfdjlpDzsnJyZk7AUdFvcCcOXOkSZMmxussVKiQHDhwwNinjX7vKeeBBOWcItQRQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAbcAQTm3CS0IRL1ATgTl9Be9+gtfU9Hwla4mlpPlVA3KpZtqiO2DDz7wDFzoKnSLFi1KH+75XUMZkyZNMvbrlqyvvPKKsS+YxunTp0urVq1cQzUEooGAw4cPu/qCaYj2oNyECRPkrrvuct3K1q1bRf+uRkMhKGd/Cn4BFIJydi9qoQvo1stTp06VvHnzGg/+5ptvREPmx44dM/bTeGoI6IqjuvKoqSQlJcmOHTtMXVab33vKeSBBOacIdQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEDALUBQzm1CCwJRL1CqVCnZsmWL8TobNGgg8+bNM/ZlpTEhIUH27t0r+fLlc03zySefyPXXX+9qz86GUz0op5Y9e/aUkSNHGlnbt29vBemMnRkaq1atKqtWrcrQ8r+PGsTTeTJTNIilfyYLFy7sOnzp0qVSp04dV3uwDdEelNOtFd966y3j7VSoUEF0672cLgTl7E/AL4BCUM7u5axpcLpv377OZlddQ0K9evVytZ/qDRdccIHMmDFDChYsaLzVH3/80dpe+9ChQ8b+7GrU1UWrV6/ue7qxY8eKBvsooQkkJibKzp07PQ/Sf08dPXrUs9/vPeU8MJqCcrptfO/evZ2X6KqvW7dOHnjgAVc7DQgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBApAQIykVKlnkRiKCArs61b98+4xl0qzfd8jESZfbs2aK//HQWXRGnTJkyAX8h7Dwm3PXTIShXrFgxyzhXLn1120uwq/rpsRp41D9DznL8+HEpV66cbN++3dnlW7/zzjvl9ddfN477z3/+Ixomy2yJ9qCchuH0l/2moiv4TZ482dSVrW0E5ezcfgEUgnJ2L2dNA7Xvv/++s9lVX758udSqVcvVfio31KtXT/Rnpb6vTUWD7LranL6Hc7r89NNP0rhxY9/L6NKli7z66qu+4xhgF7j44otl1qxZ9sb/rwWz4qjfe8o5cTQF5W677TbRn/1+ZeHChXLuuef6DaMfAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEAibAEG5sFEyEQLZJ6BhpxMnTkhcXJzrpD169JDRo0e72sPRoGEsXYHGVP7973/L888/b+rKlrbTISgXHx9vbV+q351FV2R58cUXnc3G+syZM62ghqnzoYcekmHDhpm6Arb9/PPPoqsomYquhPfyyy+buoJqi/agnN6EBuU0MOcsI0aMkPvvv9/ZnO11gnJ2cr8ACkE5u5ezRlDOKfJPXVfs/OGHH6RkyZLGARocbN68ecDtNo0HRqiRoFyEYNOm1X+nffXVV3LZZZcZT/LGG28Yt+zOONjvPZVxrH4mKOcUoY4AAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIuAUIyrlNaEEgJgR27NghxYsXd13rxIkTpVOnTq72cDToNmK6jWSBAgVc0x04cEDOO+88WbNmjasvOxpOh6Ccrroyf/58I+ctt9wi7733nrHP2XjPPffI+PHjnc1WXZ9ftWrVJDU11dhvatQVlHRVGFPRQKeuUrdt2zZTd1BtsRCUGz58uGhY1Fk2bdokZ511lqSkpDi7srVOUM7O7RdAIShn93LWCMo5RcT6e64hOf37bip//PGHNGvWTDZv3mzqzpE2gnL/sBctWlSSk5Nl//79YXsO/fr1Cxg6v+GGG+Sjjz4KeD6/95TzYIJyThHqCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggIBbgKCc24QWBGJC4Ntvv5WWLVu6rlV/2atb3a1atcrVF46GkSNHiq4QZioLFiyQdu3aeW5DaTrG1KbBqr59+8rNN98spUqVMg1xtUVrUK5y5cryyCOPWCuqqU9WyiuvvCJdu3Y1TqHb5+mqbsEU3XZVt33T8JSphLICXO7cua1Vc1q3bm2aSj7//HO59tprjX3BNsZCUK5KlSrW3znTtritWrUSXcUvJwtBObu+XwCFoJzdy1kjKGcX0RXkNCSnK8qZigZmNST3559/mrpzrI2g3D/0vXr1kqefftraXlZXAd24cWOWnok+a33n58mTxziPbnNeokQJ2bdvn7E/vdHvPZU+Lv07Qbl0Cb4jgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAt4CBOW8behBIKoFvFaw0ovWX9jrqnKR+KW8rpazdOlSKViwoNHn4MGD8uijj1rBsFBX0brwwgvl3nvvldtuu836BfPRo0clX758xvM4G6M1KFe/fn35/fffrcv95ptv5IUXXpAZM2ZYq9c47yFQXbfUHTVqlHGIBsk0UBiK9+uvvy533nmncT4NW954443yySefGPvTG3ULWA3v6Qp1XuWaa66RL774wqs7qPZYCMrpjejzNQUG33rrLbnjjjuCutdIDSIoZ5f1C6AQlLN7OWsE5f4nUqxYMZk9e7boypqmsn37dmnRooWsWLHC1J2jbQTl/uHXoJwG5LRoiO3TTz+VyZMny9SpU+XQoUP/DArivxqUfPLJJ6VDhw4SFxfnecT06dOlTZs2nv3pHX7vqfRx6d8JyqVL8B0BBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQ8BYgKOdtQw8CUS3QpEkTmTNnjuc1asjs1VdflXnz5lkrvG3YsEG0zVRCXT1FV3p79913TVOdbNNfwD/22GOyaNEi2bVr18n2jB80BKdBsrZt24puHaqrr2Usp1pQLv3edBvSDz/80Pr69ddfA/4i/vzzz5cHH3zQWqnP6xfvY8aMke7du6dPH9T3MmXKyLJly6RIkSKe4zUEN2TIENE/O85y0UUXyUsvvSSNGjVydp2sf/3119azPdmQyQ+xEpS7/vrrjVvp6XZ+uuLU4cOHMymQ9cMIytkN/QIoORGU03Bzt27d7BdqqNWtW1fy58/v6tFtjvV971eee+45+eCDD/yGBewnKPc/nkGDBlk/6/7X4v6kPwfDVfTnqWk12czMT1DuH7WMQbmMjhqS+/77760t5XXr3PQv/Z8Qjh07JhUqVJBKlSpZX7qq66233ioaIA9Udu/eLQ0bNrTmCjRO+/zeU87jCco5RagjgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAm4BgnJuE1oQiBkB3crznHPOydL1arjCa3uwQBOPGzfOWv0t0Jj0Pg2GLV++3NqaUs9XvHhx6xfL5557riQkJKQPc30/VYNyGW9UPRYvXmwFCjUQduDAAcunfPnyVnBQQzGByp49e6R69eqiqxaFWnT1Pn2OgUpqaqpomGLlypXWtSUlJckFF1wgZ599dqDDRFcWrFOnTpa34dWTxEpQTgMSf/31l2gI0Vk0CPree+85m7OtTlDOTu0XQMmJoNzDDz9sBVPtVxr+WihbK3udnaDc/2SeffZZeeihh/7XEOFP+q7X4G04CkG5fxS9gnLhMM44h676evnll1urj2Zs9/rs955yHkdQzilCHQEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBwCxCUc5vQgkDMCOiqXt99953vCiaBbiizQbm8efPKhAkTrBVUAs2flb7TISiXFR8NsXXs2FHeeeedTE2TK1cuefvtt63V/DI1gcdBunWrbp87adIkjxGhNcdKUE7vSrfde/zxx103qFv4XXnlla727GogKGeX9gugEJSzezlrupXwG2+84Wx21TUgXatWLVf7qdQQy0G5YMP2Xbp0sVaoPZWeW8Z7ya6gnIZhhw4dmvHUAT/7vaecB0dTUC6YIL5e/8KFC0X/pwkKAggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIJBdAgTlskua8yAQIQENLIwePVo0CJOZktmgXPq5NBSk4aBIFIJy3qoaktNfRL/22mveg4Lo0e1cx48fL3fddVcQo/2HHD9+3Are6day4SqxFJQrV66ctYpe7ty5bbevf8/OOuss2bJli609uyoE5ezSfgEUgnJ2L2dN3xn33HOPs9lV1/dT586dXe2nUkOsBuUKFy5sbYvufFc5n43+rNGVa3Xl01O1RDoop+//Z555RgYOHBgSod97yjlZNAXl3nrrLbn99tudl+iqZ2breNckNCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACIQgQlAsBi6EIRKtA5cqVZezYsdKqVauQLzGrQTk9YevWra1fAjds2DDk85sO0LDVzJkz5aOPPgo6CKaBjLvvvts1na6YpquuhVo0QKYro5lKmzZtZPr06aYuV5tuizpnzhwpUaKEqy+zDV988YUVTvz9998zO4XtOF1Z7t///rcMGDBAChUqZOsLpaKrR3Xr1k1mz54dymG+Y2MpKKc3oystmoKHI0eOlN69e/vebyQGEJSzq/oFUAjK2b2ctTVr1lhbQzvbM9aPHTsmVatWlfXr12dsPuU+x2pQ7qqrrhL9WeJXdGXQm2++2W9YTPdfccUVou9n/bdUuMuSJUvkzjvvlLlz54Y8td97yjlhNAXl9O+9biEfqBw5csQy37x5c6Bh9CGAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACYRUgKBdWTiZDIGcFypYtKxri0i/9hW9iYqL1VaRIEdHgl6ls2rRJdBWscJTrrrtO+vTpI40bN5Y8efKENOXBgwdl2rRp8sknn8iUKVNk7969IR0/ePBgefTRR13HPP/881YIzNXh06DhsW3btklSUpJrpK6us2jRIle7V4POpVuLaZBRQ4VNmzaV/Pnzew33bP/888+tgNy8efM8x2Slo2TJkvLUU0/JTTfdJLraULDlzz//lBdeeMEKa2rwMtxFgwa1a9d2TauWM2bMcLXndEPp0qVl9erVUqBAAdul5GQogKCc7VGIXwAlJ4JyPXv2tMI69isNf619+/bywQcfZHpiDb8EE357+eWXRe/pVC/6M0/ff9lVdGW3evXqZfl0+rPxgQceCDiPhsX13bty5cqA406VzgoVKsill15qfV1yySVSqlSpTN2auum/Y3S1NA3V66p8mSl+7ynnnNESlNN/f2qY1q/o35u+ffv6DaMfAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEAirAEG5sHIyGQIIqICuStaiRQtp2bKlFcLT1dQ0cKbhiooVK8qGDRusL63r57/++kt++eUXOXz48GkBmDdvXmnSpInUrFlTNFRVpkyZk19qom3r1q2zfS1btkzWrl2bLT4acmzWrJm0bdtWzj77bDnzzDOtL/3Ft4Yqt2/fLrrK2/z58+XLL78UXUmOYhfw2pJYt0nu0aOHfXA21AIF5TT0eeDAAeNV6KqOkQwBaYBU/xyZyq+//ipewcsXX3xRsrK9r18AxSsodyL5hKzatMR0uVbbuC+fkxUbzFtUfjMk+HCt5wmioEO3U9RtFQMVfZdrWCanthoOdG30/SOggevzzjsvIIc+Z93e/XQtGhLUUKKGyDU0p9/Tv3bv3m191i3i9+zZIzt37pQFCxZY/5bR1eP27duXZTbTe+rutvdLnYrm51Y4f1Epl1TRdd69B/dI+8EtXO2mhnC8p3RbZt2eOVDR/zmiUqVK1r8lAo2jDwEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBAItwBBuXCLMh8CCCCAwGkvoMG0VatWia7ymLFoqEK3o9SAaHaWQEG5QNcR6WBfoKBcoOvSLWx1q8TMFlMAJeNcXkG5jGNMnx97s5vMXfWjqUvCEUAxTpzNjW+++aZ06tQp4Fkzu5JnwEnpDJuArjarYWevlWb1RLoFum4driuGUnJGwPSeerLjSLmwZnCht/Srzu6g3Lvvvuu7Xe+QIUOMqwCnXzPfEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIFICRCUi5Qs8yKAAAIInNYCGibSUJGzjB07Vu677z5nc0TrBOXsvKYASsYRBOUyatg/6wqgZ511lr0xQ01XJ9SVonbs2JGhlY/RJNCuXTv5+OOPA17SuHHjpGvXrgHH0BlZAdN7KhaCcps3b7ZWxvXS2bt3r/WO0FX5KAgggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBAdgsQlMtucc6HAAIIIHBaCOTKlUt0C7769evb7vfYsWNSrVo1a8thW0cEKwTl7LitH64naY/Hs4Q7KJeaKjL92djfelW3YvbbAnrw4MHSv39/T1s6cl5AV2Ps2bOn54XoypdVqlSRjRs3eo6hI/ICpvdUJINy4XhP1ahRw3c79oEDB8pTTz0VeUDOgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggYBAjKGVBoQgABBBBAIBwCF198scyaNcs1lQZVdPvQ7Cr58+eXgwcPpoXDAqTDDBcT6a1X69WrJwsXLjScOXBTVrdebf1QWlAuzvsczeq0lv63POc9wKPHa+vV1JS0oNzQ2A/K3X333fLaa6953L3Inj17rJWi9DslegUWL14sderU8bzAESNGyP333+/ZT0f2CJjeU33/9ZS0aXBtSBcQ7Nar4XhP6WqpY8aM8by+nTt3Wu+I/fv3e46hAwEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBCIpABBuUjqMjcCCCCAwGkvoCvKJSQk2Bw0LLB69WpbW6QrukJUiRIlQjrN1q1bZd26dSEdE+rgunXrSoECBUI6TK9Jry2zpVW/uhIX7x0a1EBhlTI1JT4uPqRTrP/7Dzl45IDrmJQTqTJj+GJXe6w1vP3223Lrrbd6XvaAAQNk0KBBnv105LxAUlKSbNu2zTM0e+jQIdGVA3UMJWcFTO+pfAn5pVLJqiFd2ImUE7J60zLfY8Lxnpo8ebLceOONnud66KGHZNiwYZ79dCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACkRYgKBdpYeZHAAEEEEAAgagSuKRvHYlPCLCkXJiv9sSxFPn2+SVhnjX7p9u0aZOUKVPGeOIdO3ZYAStWijLyRE2jhpg0zORVNMSkYSZKzgvE2ntKA8YasNQwpqlon4YwNYxJQQABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQRySoCgXE7Jc14EEEAAAQQQyBGBi3vWkoSCoa0Wl5ULPXbghMwe5b+iU1bOEeljq1evLitWrPA8zYMPPijDhw/37KcjOgReeeUV6dq1q/Fi9u3bZwWZdMVLSs4LxNp7SlcHXbTIe4tp3c5Xt/WlIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAAAI5KUBQLif1OTcCCCCAAAKnsUBCgXgpWb2IFClzhhQskU/yFsojefLllly5c0lqcqocP5IsR/cflwM7jsjezYdk28q9cuzgiSyLXdS5etr58mZ5nmAnOLDjqPw0fmWww6NynIarNGRlKroNbuXKlVkpyoQTZW0adtTQo6k89dRTMnDgQFPXad3Geyq4x9+rVy/PINzGjRtFt/8+evRocJMxCgEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBCIkABBuQjBMi0CCCCAAAIImAXyF0mQys1LSqmaRSUuLRQXbElJC89tXb5H1n6/TQ7vPRbsYa5xDW89WxLPKuhqj1TDrvUHZO47f0Rq+myZ97XXXpO7777beC4NyIwaNcrYR2P0CBQpUkR27dolcXHubYd3794tlSpVkr1790bPBefwlfCeCu0BTJw4UTp27Gg8SIO248aNM/bRiAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAghkpwBBuezU5lwIIIAAAgic5gJnNSwh1VqWlrj44ANyTrKUE6myatYWWT93h7MrqHqtK8pJuXMSgxobjkEbF+ySZV9tDMdUOTbHmWeeKY0bN5bU1NSTXykpKdbnb7/9lpWicuzJhHZifYb6LPU5pj8//bx582ZZsGBBaJOdwqN5T4X+cEuVKiUXXHDByfdDxj9jM2bMkOPHj4c+KUcggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAmEWICgXZlCmQwABBBBAAAG3QK64XFI7LaBWpm4xd2cmWzYv3i1Lp26U1JTUkGYoe26i1L68XEjHZGXw0rSQ3Ka0sBwFAQSiW4D3FO+p6P4TytUhgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBAVgUIymVVkOMRQAABBBBAwFegzlXlwxqSSz+hhuWWTNmQXg3qe0KBeGnevWZI274GNbFhkG4X+/3o5XLs4AlDL00IIBBNAryneE9F059HrgUBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQTCL0BQLvymzIgAAggggAACGQR0G8MarctkaAnvxxXTN4e8DWvda86S0rWLhvdCDLNtWbpHFn++3tBDEwIIRJMA7yneU9H055FrQQABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgcgIEJSLjCuzIoAAAggggECaQP4iCdLk3uoSF6//5IhMSTmRKj++ulIO7z0W9AnyF027rs7ZcF3j065rT/DXFfQNMBABBMImwHuK91TY/jAxEQIIIIAAAggggAACCCCAAAIIIIAAAggggAACCES1AEG5qH48XBwCCCCAAAKxLVDn6rQtV+sUi/hNbF6StgXrF6FtwVrx/CSpdmnpiF3bqplbZN2vf0dsfiZGAIHwCPCe4j0Vnj9JzIIAAggggAACCCCAAAIIIIAAAggggAACCCCAAALRLkBQLtqfENeHAAIIIIBAjAoknJFbmveoJXG5I7eaXDpNSnKqfP/yMjl2KDm9KajvkdqClS1Xg+JnEAI5LsB7ii1Xc/wPIReAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAAC2SZAUC7bqDkRAggggAACp5dA2XqJUvvKctl200u/3CibFu0K6Xy54nJJnavKS+naRUM6LtBgDcktmbJBUlNSAw2jDwEEokCA9xTvqSj4Y8glIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAQDYJEJTLJmhOgwACCCCAwOkmUOvyslLu3OLZdtsbF+yUZV9tytT5dBvWKi1KSVx85le/SzmRKmu+28p2q5l6AhyEQM4I8J7KGXfOigACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgjkhABBuZxQ55wIIIAAAgicBgINbz1bEs8qmG13umv9AZn7zh+ZPl/+oglSpXkpKVmjSEjbxeq2r9tW7JU132+Vw3uOZfr8HIgAAtkvwHsq+805IwIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCQUwIE5XJKnvMigAACCCBwigtc1Lm6FCyR13iXpRPLy4U1Wxj7AjX+vHy2bNm10TjkwI6j8tP4lca+UBoTCsRbYbkiZc6QgsXzSd5CeSRPvtySK3cuSU0LxR0/kixH9x+XAzuPyN7Nh6yQ3LGDJ0I5BWMRQCBKBJzvqQtqNJcyxc8K6epOJB+XL35+P6hjeE8FxcQgBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQCAiAgTlIsLKpAgggAACCCDQomctyVsw3ghxUa1LZOBtLxr7AjU+8Z/e8t+0sJypHD1wQr4btczURRsCCCBgFHC+pwbc8rw0rdPKONar8dDRA9LuySZe3bZ23lM2DioIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAQLYKEJTLVm5OhgACCCCAwOkjcEnfOhKfEGe84UgE5U4cTZFvX1hiPB+NCCCAgEnA+Z6KdFCO95TpKdCGAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCGSPAEG57HHmLAgggAACCJx2Aq361ZW4eP2nhrtEIiiXciJVZgxf7D4ZLQgggICHgPM9FemgHO8pjwdBMwIIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCQDQIE5bIBmVMggAACCCBwOgq0fqie5DIvKCeZDcoNnNhLfl7xnZEzNUVk+tBFxj6/xqJFi0qpUqWkZMmSkpqaKnv27JHdu3fL33//LUeOHPE7nP4oFIiLi5OkpCQpXbq0JCYmyr59+2TTpk2ybds2SUlJ+8NCiUqBhIQEKVGihPWlz+348eOyZcsW2bp1qxw6dCjs1+x8T0U6KJeV91TYb54JEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIHTTICg3Gn2wLldBBBAAAEEskug9cNpQTnzgnIBg3IfzZlovMQjxw7LpNkT5NiJo8b+tHybTH/WOyinwam6detKgwYNrK9zzjlHypYtawXk8uXLZ5wzOTlZFi1aJD///LNMmzZNvvzyS9E2SvQJ5MmTR1q1aiU33HCDtGnTxgrI5c6d23WhJ06csEJXa9eulalTp8qnn34qq1atco2jIfsEzj//fLn66quldevW0rBhQzE9N72avXv3yvfffy+TJ0+Wzz77TPbv35/li3S+p1qdd7WcXbqacd7SieWsd5ez89DRA9LuySbOZmPd7z1lPIhGBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACBsAgQlAsLI5MggAACCCCAgFOgzSP1nE0n614ryh04sl/+9VTTk+NC/fDNEHtQrmDBgnLfffdJixYtpGnTplKkSJFQp7SNX79+vYwePVpGjBghR4+aA3u2A6hEXEBDjgMGDJBu3bqJrgyYmbJixQp58803ZejQoZk5nGMyKaChuEGDBslll10W8gy60uPIkSPl8ccfz9LfxUDvKedFNa55sTzRcYSzWUIJyunBzveUa0IaEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEIiJAUC4irEyKAAIIIIAAAoECKNkVlKtWrZqsXLky7A9j6dKlcvvtt8u8efPCPjcTBi/QsmVLGTdunFStWjX4gzxGaliuZs2aHr00h1ugf//+8vTTT2d52mXLlkmnTp1k7ty5mZor0HvKOSFBOacIdQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAgtgQIysXW8+JqEUAAAQQQiBmBQAGUWA/K6UM4fvy4tG/f3tq6M2Yeyil0of369ZNhw4aF7Y4IyoWNMuBEuq3q+PHj5c477ww4LpROXV3u4osvll9++SWUw6yxgd5TzskIyjlFqCOAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACsSVAUC62nhdXiwACCCCAQMwIBAqgnApBOX0Qx44dk2uvvVamTZsWM8/lVLjQzp07y6uvvhrWWyEoF1ZOz8kGDhwoTzzxhGd/Zju2bdsm559/vuj2yKGUQO8p5zwE5Zwi1BFAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQACB2BIgKBfB51W5QkEpX/YM2xkWL98rO3cftbWFWsmVK5fUq1XEdtjhw8my6o/9tjYqCCCAAAII5KRAoABKNAXlDh48KJs2bZKNGzfKli1bJF++fFKxYkWpVKmSJCYm+hLu3r1bdIvXHTt2+I5lQNYFbrjhBnn//fclLi7OdzLddvevv/4SDVEVKFDAeqZnn322FCli/3eUTkRQzpczywOaNWsms2bNEl1Vzqvs2bNHfvrpJ1mwYIEsWrRI8uTJI+edd540bNjQCsLp30+vosfouOTkZK8hrvZA7ynnYIJyThHqCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggEBsCRCUi+Dzuv6KcnJps1K2M4x5c7UsXbnX1hZqJT53LhkxqIHtsI1bDsuQkUttbVTMAnFxuaRYkQQpXiztKzGvFC+aIAUL5pH9B47L9r+PyvadR2T7jiNy+Ejwv2Q1n4lWBBBA4PQWCBRAyamgXGpqqvz222/yxRdfWKvArVmzRjSY41Vq164tTz/9tLRr185riNX+2muvia5yRomsQFJSkqxatUqKFi3qeaK///5bRo0aJZMmTZLVq1cbx2kAUkNbPXr0kEsvvVT0f0IgKGekCmujBt/q1q3rOeecOXPkxhtvlK1btxrH1KlTx9rquHLlysZ+bbztttvknXfe8ex3dgR6TznHEpRzilBHAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBGJLgKBcBJ8XQbkI4mZi6krlC8gF9UtI/XrFpMAZ8b4zbNl2WD6dtkmWrPAOUPhOwgAEEEDgNBYIFEDJrqCcBqJ+/fVXWbp0qRWOmzJlimcIJ9Cj0i0dNVx35plnGofpClalSpViVTmjTvgax44dK126dPGc8OOPP5auXbuKhuWCLTVq1JDu3btb2+j27ds32MMYF6LAJZdcIjNnzvQ8SsON6n/8+HHPMdpRrFgxeffdd6Vt27bGcRp41IBrSkqKsd/ZGOg95RxLUM4pQh0BBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQiC0BgnIRfF4E5SKIG8LUl11cWho3KC5JJby36go03YrV++SjqRtk89bDgYbRhwACCCDgEAgUQMmuoJzjkrJUvfzyy+XLL7+0Vh8zTXT33XfL66+/buqiLQwC55xzjvz++++e23a++OKL8sADD4ThTEwRCYFPP/1Urr32WuPUc+fOlUaNGhn7TI3x8fHWCoBeK8vddNNN1va8pmOdbYHeU86xBOWcItQRQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgdgSICgXwedFUC6CuCFM/dJT9SVPnrgQjnAPTU1blGTk66tk1dp97k5aEEAAAQSMAoECKLEYlNOb1FWvdLtOU5k8ebJ06NDB1EVbGAQ++ugjuf76640zffjhh9K+fXvRrXUp0SegwTbd4rhAgQLGi9Nn98EHHxj7vBrvvPNOz2CqhvL8tktOnzfQeyp9TPp3gnLpEnxHAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBGJTgKBcBJ8bQbkI4oYwdTiCcnq6/fuPy+CRy2T/gcBbgoVwaQxFAAEETmmBQAGUWA3KtWjRQmbPnm18bj/99JM0adLE2Edj1gQKFy4s27dvl7x587om2rFjh+jKYvv2EWZ34URJQ/369a3VAE2Xs2bNGqlevXrQW6Wmz6Hhu5UrV8rZZ5+d3nTy+/79+6V48eK+27jqAYHeUycn/P8PBOWcItQRQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAgdgSICgXwecVqaBcXFwuue1fFW1Xvnj5Hpm/ZLetjco/Aq6gXNpiM8vTtlNdt+GAbN522PravuOoFCwQL6WS8knTC5KkQb1EI9/KNftk1OurWbHGqEMjAgggYBcIFECJ1aBcYmKi7Ny5036j/1/7448/rMCWsZPGLAl07NhRJk6caJyjT58+8tJLLxn7aIwOge7du8vLL79svJgnnnhCnnzySWOfX6M++xdeeME4rGXLlp6h1owHBHpPZRynnwnKOUWoI4AAAggggAACCCCAAAIIIIAAAggggAACCCCAAAKxJUBQLoLPK1JBuQhe8ik5dXpQLjklVeYu2CXffLdFtm4/EvBea1QtLPfeWiVt5Rr3lq2vvLVGlqzYE/B4OhFAAAEEAq/UFKtBOX2uunJZoUKFXI9469atUrp0aVc7DVkX+PLLL+WKK65wTbRt2zapUKGCHD161NVHQ/QIDB06VB588EHjBd1xxx3y1ltvGfv8Gi+77DKZNm2acZie8+GHHzb2ZWwkKJdRg88IIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAwKktQFAugs+XoFwEcUOYuudd1WTb30dk+g9bZfeeY0Ef2aLxmdL+mrNc47+auVmmzNjsaqcBAQQQQMAuECiAEqtBOd3OUbf6NJWff/5ZGjdubOqiLQsC+fLls8KJefLkcc0ybtw46dq1q6udhugSePXVV6Vz587Gi7r00kvl22+/Nfb5NeqWrStWrDAOmz59urRp08bYl7Ex0Hsq4zj9zIpyThHqCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggggEBsCRCUy+LzypUmmJq2laepxHpQLtC9ZbzfYMdlPCYWPut9Pda7tpQumd92uUtW7JVX3lpta6OCAAIIIOAWCBRAidWgnAZvvv76a/fNprXoqli6OhYlvALnnXeezJs3zzhp27ZtPZ+H8QAac0Tggw8+kBtuuMF47mrVqsnq1Zn7d5WGKA8dOiS59B9tjvLnn3/K2Wef7Wh1VwO9p5yjCco5RagjgAACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAArElQFAuxOdVonheaXROcalTo4gkFk2QQgXzSHJyquzee0y2bD0s/523I21bzr2SkrbNZ1aDcuVKnyFFiyQEdYVHjiTLmnX7gxqbPuiihiXSP1rf/9xwMG3rshS5sEFxqVW9iFQsW0COHEu2tin97qft8tvCnda4uLhccl6dYlIzbXvSmlUKS+HCCbJx80H5Y/0BmfPL37LFZ1tT20mjvHLr9RXlokZ2p737jsujQxZG+ZVzeQgggEDOCwQKoMRqUO7dd9+Vm2++2Yj76KOPypAhQ4x9NGZe4PbbbzduzXn48GEpUqSIHD9+PPOTc2S2COj2qLpNqqnoM9TtjDNbdu3aJcWKFXMdnpycLGeccYYcOxZ4NeFA7ynnpATlnCLUEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIHYEiAoF+Tzio/PJVdcUkZatygtcXGBD1q/6ZC89s5aadE4SS5tVso2eMybq2Xpyr22Nq9Kp/aV5Pzzint129o3bjksQ0YutbUFqujKGy8/08A2ZPr3W6VB3URJLGYO5838YZt8/s1GuffWKlI7LShoKgcOnpAXX11hhetM/bHW1qp5KWl3eTnXZfd49Pe0lQQ9lhJ0jaYBAQQQOD0FAgVQYjEo17t3b3nppZeMD1NDOfXq1ZNly5YZ+2nMvMDw4cPl3//+t2uC+fPnS/369W3tSUlJ0qlTJ6lVq5ZUqlRJKlasmPbvtjhru9y///5bNmzYIDNnzhTdlnPnzn/+BwDbBFQiIvDxxx9Lu3btjHNXqFBB1q9fb+zza8ydO7ccOXJE4uPjjUNr1KghK1euNPalNwZ6T6WPSf9OUC5dgu8IIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAQGwKEJQL4rlpSK77HVWlWuXCQYz+Z4gGxlat3S/169lXuIjmoFwwN7f6j/1S9exCAYfqimsDn1uctsJLSsBxsdDZJi0YeW3bsrZLPXQoWfo9Pd/WRgUBBBBAwC0QKIASS0G5PHnyyJ133imjR4/2DOSMGzdOunbt6kagJcsCXquRvfPOO3LbbbdZ81euXFl0Rb9bbrlFdDtOv5KSkiJTp061jlm8eLHfcPqzKDBhwgS56667jLM0adJEfvrpJ2OfX6OG7NatW+c57Morr7Ses+eAtI5A7ynncQTlnCLUEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIHYEiAoF8TzuueWynJeXXvgLYjDjENiPShnvClD45vv/ym/LYj9lVru6HC2NDo30XaHoa7eZzuYCgIIIHAaCQQKoMRCUK5o0aLSvn17eeSRR6yVybwe3f79aSHyqlVl27ZtXkNoz4LA8uXLRVcGc5b+/fvL4MGDpWXLlqIrlunzCrVoYO61116Tnj17+m7RGercjP+fwPPPPy8PPPDA/xoyfOrQoYNMnjw5Q0vwH1u0aCGzZ8/2PKBjx47y9ttve/ZrR6D3lPNAgnJOEeoIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAQGwJEJTzeV4VyxeQft1qGkctX7VP1qzbL9v+PiKlkvJJlUqFpEbVwKvOhRKUu7BBCamUdn5niYvLJRc1KmFrDjW8Zdp6VSfU1eA++3qjHD6cLE3PT/LcYnXHzqMy9dvN1rjmF54pNavZ7/vn33fIfz5cd/IaCxfKI0/2q3uyHokP+hyeHRW+Le/yxMfJs/3PkXx5c9sud9aP2+TDKRtsbVQQQAABBNwCgQIo0RCU69y5s1x88cW2Cy9UqJDoKlX6VaSIeZvxjAdoOO6aa66RX3/9NWMzn8MosHHjRilb1r66q06vq/wdP35c3njjDdFV/7JSvv32W2tr0H379mVlGo71EOjXr58MGzbM2Kvb6mqQLjNF/wy8/vrrnofqKo+62mOgEug95TyOoJxThDoCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgggEFsCBOV8nleXjlWkXi3HCiWpIu99+pfM+fVv19HNLkiSm66tIKKyhhJKUM5wuNUUnzuXjBjUwNYdrqCcBs02bD508jwPdq8lZUvnt53rxIkUefrFpbJj11GrPSFPnAzoU0cSiyWcHLd562EZPGLpyXqRwnnkmUfOOVmPxIet24+kXdeSsE3dsklJueGq8q75ho9ZLus2HHS104AAAgggYBcIFECJhqCcriR299132y86hNqSJUvk6quvDrj1YwjTMdRDYO/evVK4sD2Qr0MHDBgguqpc3rx5PY4MrfnHH38UXaEsOTk5tAMZ7SuggdRZs2YZx82YMUNat25t7PNr1NXk9Jl5FV3F7sUXX/TqttoDvaecBxKUc4pQRwABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQRiS4CgXIDnVbpkfunfu7Yr9Pbux+vkx992eB55UcMScuu/Khr7ozkot2XbYRn00v/CbXoDV7UuI5dfUsZ2L8tW7ZXRb6y2td10XQXRkGB6OXYsRfoMnJdelVgLyhUqkEcG/ruO5M9nX03uz7SA3HNpQTkKAggggIC/QKAASqwG5VJTU+Wbb76RV155RaZMmUKoyv+PQZZG6Aq4J06ckLi4uJDm0ZXmli5dKmvXrrVWB6xTp47ky5fPd46BAwfKU0895TuOAaEJFCxYUPbs2SO5c9v/XZU+S/369WX+/Pnp1aC++227qpOkb88baMJA7ynncQTlnCLUEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAAIHYEiAoF+B5dWpfSc4/r7htxN9pW44+8dxiW5upoiuslTrT/QvZaA7KzVu0Wya8t9Z2O3r/6pCxzPxhm3w81b716KXNSsr1V9hXX3vgiXly9GiKdWisBeW63l5F6tZ0ryQ4/BVWk8v4Z4HPCCCAQCCBQAGUWA3K7d69W/r06SPvv/++HDlyJNDt0xcGAQ1Y7d+/P+iZFi1aJN26dZO5c+em/Rvkn5Vv9WANaNWuXVtGjRolzZsShtuVAABAAElEQVQ395xPQ3nnnnuuFbLzHERHpgR0e+JGjRoZj500aZLcfPPNxj6vRl2hzrl1snPskCFD5NFHH3U22+qB3lO2gWkVgnJOEeoIIIAAAggggAACCCCAAAIIIIAAAggggAACCCCAQGwJEJQL8LyefqieJBb933aiOvSLbzbJtFlbAhz1T5cpOKY90RyUm/3jNvlgij0AV6NqYel5VzXb/WpITsNyGcsFaYG62x2BuoHDF5/cnjVf3tzStmXpjIeE/fOKNftEv7JaWjUrJe2uKOeaRrfafe+Tv1ztNCCAAAIImAUCBVBiNSiXfqc7d+60VpV78sknrRXP0tv5Hl6BkiVLytatW4OaVMNWupXuoUP/bCFvOig+Pl6GDRtmhR1N/do2fvx4uffee726ac+kwD333GPZmg7X7W6vueYamTp1qqnb1fbwww+LhuD8yksvvRTwWevxgd5TzvkJyjlFqCOAAAIIIIAAAggggAACCCCAAAIIIIAAAggggAACsSVAUM7jecXF5ZIRTzdI2+rLPqD/0EWye88xe6Ohplt3Dnn0HMnlOD6ag3JTZ26WL2dstt1NxfIFpF+3mra2tz9aJ/+da996tl7a6mtd0lZhy1iefnGJbN0eW6vtVK1USHrdU9313DduOWxtuXr8xD8r5GW8Tz4jgAACCJgFAgVQYj0ol37H06ZNkxtvvFEOHDiQ3sT3MAokJSXJ9u3bfWccOXKk9O7d23dc+oBBgwbJY489ll61fdegXbly5URXD6SETyB//vyyceNGSUxMNE6q2xpr8PTpp5+WlBTzv7cKFCggr7/+urRv3944h7Pxueeek379+jmbbfVA7ynbwLQKQTmnCHUEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBAILYECMp5PK9iaSvJDUpbUS5jOXQoWfo9PT9jU8DPerzOk7FEc1Dus2mb5Jvv7KvllS19hjzaq1bGW5A3Jv0hcxfusrXVrFZYetxpX3luyMhlsnGL96outgmioKJb5fbtWlPOyJ/bdjWHjyTLs6OWnVwdz9ZJBQEEEEDAUyBQACUagnJ58+aVPHny2K6/cOHCUqVKFeurcuXK1jadTZs2tY1xVn7//Xe58sorZds2+2qrznHUQxfQYJRfCHHv3r1SqVKlkIJtGtZav3696Pym0qlTJ5k4caKpi7YsCPTt21c0vBaozJ49Wz7//HNZsGCBLFy40Po72qBBA2nYsKHcdNNNUrOm/X/gCDTXwIED5amnngo0hBXlAurQiQACCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgicWgIE5TyeZ+WKBeWBLjVsvbo6mq6SFmx5MG0ltgppK7JlLNEclDNtqVo6LTzWv0+djLcgr72zVuYvsa+yUr1y4bSV2OxBueFjlsu6DQdtx0ZrpUjhPPLv+2q6ttpNTkmV0a+vlpVrs76la7TeO9eFAAIIREog2oNywd63huCGDh0qtWvX9jzkl19+kQsvvNCzn47MCeTKlctzdbH0GTUIpYGoUMuLL74o999/v/GwsWPHyn333WfsozHzAvo8Z86cKS1btsz8JI4jNaCakJAgxYoVc/SIPPDAA6LPOVAJ9J5yHseKck4R6ggggAACCCCAAAIIIIAAAggggAACCCCAAAIIIIBAbAkQlPN4Xo3OKS533FTJ1rv6j/3y0viVtrZAla63V5W6NYvYhkRzUO7DKRtk1o/21XBKlsgnj/8fe/cBZldV7g/4S4eEGloCIZAQAgmEJhCadJDmRS5VMEDgjyIKSAeFIGIoQhQEAelNkCKiV5EuvXdCgNBCCURIg/Q28589Ojhz2pyTnJk5+8y7nyfPnL3W2uus/X6Tbe7D7659QtOg3JU3vxuvvjG1yX0N7L9kHHvEWk3a0hKU675457pQ5FrRe6XFm6w/aiOuv/2DeP6VSU3bnREgQIBAUQKFAiiVsKNcUTfxn0GdOnWKU089NZJXduY79tprr7j77rvzdWtfSIHkVajJaztzHdOmTYu+ffvG1KlN/12Sa2xm22qrrRbjxo3LbK4/T3Yz23DDDXP2aVw0geS1to899lj9LoCLNtO/r05ew3rppZfGiiuumDXdEUccEVdffXVWe+OGQs+pxuOSz4JymSLOCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQLpEhCUy1OvHb/ZK/barU+T3lKDct/be/XYfOPlm8whKNeEo81PunbpWLcT3lrRr2/Tnf+SheXaYa/NF2wBBAgQSJFAoQBK2oJyDey333577Lvvvg2nTX6OHj061l9//WZ3QGtykZNmBSZOnBjLLbdcznFPPPFEfPOb38zZV0xj8trW5HW7mce8efPqdynLbHdeHoFevXrF3//+99hoo40WesL58+fHUUcdFVdddVXkC1Mmr2q97bbbCn5HoedU5oWCcpkizgkQIECAAAECBAgQIECAAAECBAgQIECAAAEC6RIQlMtTr603WzH237Nvk97X3/wyrrjxnSZthU4O2a9fbLph0/+w216Dch07doilluxSiGuR+2rqXpP61bR5Rc/TqW5NRx4yIAYPbLrrXzLBA49OiLvv/aTouQwkQIAAgWyBQgGUtAblklDVyy+/HP3798++4bqWvffeO+66666cfRoXTuDDDz+s3zUu19U333xzDBs2LFdXUW1vvPFGDB48OOfYJZZYImbMSMcr5HPeQIU3Jr4jRoyIo48+OhZbbLGSVpvsILj//vvH/fffHyuvvHKMHz8+5/W77LJL3HfffTn7GhoLPacaxjT8FJRrkPCTAAECBAgQIECAAAECBAgQIECAAAECBAgQIJBOAUG5PHVbd+1l4od1IarGxwcfz4gLL3uzcVPBz0cfNjDWXrPpLiXtNSi39FJd4pzT1i/otaidEz6fHWf/ZnRR03To0CGG171a9xvr9cwa/9TzE+MPd43LatdAgAABAqUJFAqgpDUolwgceuihcd111+XEOPfcc+OnP/1pzj6NCyfw9NNPx2abbZbz4uRVuGeccUbOvmIa77333vjWt76Vc+iqq64an3wiNJ8Tp4yNyatYf/KTn8Ruu+0WgwYNKjhz8qrc3/72t3HNNdfEV199VT92xx13jAceeCDndauvvnokQctCR6HnVOZ1gnKZIs4JECBAgAABAgQIECBAgAABAgQIECBAgAABAukSEJTLU6+Vey0ePzt2nSa9k6bMjRG/eq1JW6GT5PpknsaHoFxjjfJ+LiUo9929VoutNl0hawEvvTYlrv3j+1FbW5vVp4EAAQIEShMoFEBJc1BuyJAh8dpruf89cMcdd8R+++1XGpTRBQVuuumm+N73vpdzzBFHHBFXX311zr5iGpPA1WGHHZZzaFLn5HW6jtYT6Nu3b2ywwQaxwgor1P9Zcskl4/PPP48PPvig/s+YMWNiwYIFTRaU7EiXhOcyjyRIt/TS2bsGZ44r9JzKHCsolyninAABAgQIECBAgAABAgQIECBAgAABAgQIECCQLgFBuTz1WnyxTnHhmRs26Z07ryaOG/FSk7ZCJ+efvkEs0aNzkyGCck04ynpSbFDuO7v2iZ227pX13WPG1r1a94Z3Y0HdK1wdBAgQILDoAoUCKGkOynXu3DmmT58e3bp1y0J65ZVXYsMNm/77IWuQhpIEktdznnXWWTmvWdSg3LXXXhvDhw/POfe6664byatZHZUtkLx+96CDDspa5FNPPRVbbrllVntmQ6HnVOZYQblMEecECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgXQJCMoVqNeon28Yi3Xr1GTEr3//Vrw3bnqTtlwnq/XpESf/KPv1UYJyubTK0/bJZ7Pi3N8W/g/a39q2d/zPt1bJ+sKkppdcOzbm1YUhHQQIECBQHoFCAZQ0B+USnffffz/69euXBTVt2rRYaqmmr13PGqShJIEDDzww/vCHP+S8ZlFfvXrffffFzjvvnHPu5JWg48ePz9mnsTIEunTpUr/j3DLLLJO1oN///vdx5JFHZrVnNhR6TmWOFZTLFHFOgAABAgQIECBAgAABAgQIECBAgAABAgQIEEiXgKBcgXqddsw60ad301enPvXCxPjDn8YVuOrfXQfsuVp8c7PsV3u216Bcly4d4xvr9WzWbVEGjPt4eiS7yuU7ttlsxdhvz75Z3R99MiMuvnpszJ7T9FVeWQM1ECBAgEBJAoUCKGkOyiXhnGRHua5du2Z5zJs3L2d71kANRQtssskm8dxzz+Ucn+wmNmzYsJx9xTQmO8YNHjw459AePXrEzJkzc/ZprAyBnXbaKe6///6cizn88MMj2TGwuaPQcyrzWkG5TBHnBAgQIECAAAECBAgQIECAAAECBAgQIECAAIF0CQjKFajXdluuFPvssWqTEXPm1MSpI1+J5DWs+Y4kFHbuT9eP5PWtmUd7DcplOrT2+dCNlouD96nb+Sf5jW90fPavWfGbK9+OGTPnN2r1kQABAgTKIVAogJLmoFzySs7XX389J9HkyZNjueWWy9lXSuPQoUPjlFNOafaSCRMmxFFHHdXsuDQPSIKJkyZNiiWXXDLrNp588snYaqutstqLbfjqq69yzjtnzpxYbLHFip0m77izzz471llnnbz9DR1XX3113HPPPQ2nfhYpcNttt8V+++2XNToJrPbq1SuSv4/NHYWeU5nXVlJQLnmt7AknnJC5xKzzTz75JI455pisdg0ECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgfYoIChXoOrdunaMX56yfnTv3jTw9tqYqXHNLe/F/AW1WVd37twh/t+Ba8SQQdmvgEoGC8plkbV4wwbrLFtfkw4dm37VF5PmRPIq3a+mzWva4YwAAQIEyiJQKICS5qBcoVeBvvTSS/GNb3xjkf2+853vxJ///Odm53nvvfdiwIABzY5L+4DEIjHJPJKd/fr27RtTpkzJ7Gr2PHl1bvIK3VzHCy+8EMlOdot6PPLII7HNNts0O83RRx8dl156abPjDPivwEYbbRRJnTp0yPj/gqgb8ve//z322GOP/w4u8KnQcyrzskoKyiUBwSQo2Nzx5ptv5t01sblr9RMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEqk1AUK6Ziu6x48qx6w4rZ40aM/bLuP62D5rsRNaje+cYvn//GDRwqazxDQ2Ccg0SrfNzpeUXi5/9ZJ3o1Cn7P6I+9/KkmDx17kIt5KnnJ8akKXMW6loXESBAoL0IFAqgpDUol7xu9emnn44kpJPrGDlyZJx++um5ukpqE5RryvWDH/wgrrjiiqaN/zn75S9/GWeccUbOvkKNl1xySfz4xz/OOeS3v/1tHHvssTn7SmkUlCtFq/ixHTt2jAceeCC23377nBcdfPDBcdNNN+Xsy2ws9JzKHCsolyninAABAgQIECBAgAABAgQIECBAgAABAgQIECCQLgFBuWbqtUSPznH2KetF17rXqWYddRvKffb5rPjXF7NjpRUWi94rLp71as/Ma4oJym2x8fKxx06rZF7a5Hzppbo0OV9QUxvTpxd+fejlN7wTH386s/66ZPeNS89puuPNnX/7OP755L+azJsEzUacsG6TtitvfjdefWNqk7aB/ZeMY49Yq0nbBZe9GeM+ntGkrbVP1lh9iTj+B2uX/WsvuurteOf9aWWf14QECBCoJoFCAZTWCMotv/zyMWPGjJg1a1bZWH/zm9/ET37yk7zzbbHFFvVBurwDiuwQlGsKtdpqq8W4ceOaNv7nLHl96uqrr17SrnLJ78aHH35Yt2tw95xz7r///nH77bfn7CulUVCuFK3ix44aNSqOP/74nBckr1tNfh+mTSvu32mFnlOZXyAolyninAABAgQIECBAgAABAgQIECBAgAABAgQIECCQLgFBuSLqNXTD5eLgffs1G4IrYqqiXr267RYrxr7f7lvMdCWNaRxcE5QriS5rsKBcFokGAgQIZAkUCqC0RlDuvPPOi8MPP7z+lZa/+93vYuLEiVlrLKXhf//3f+POO+/M+arHZJ5JkybFiiuuGDU1NaVMm3OsoFw2S/I6zd122y27o64leW1p8vrSYo/zzz8/Tj755JzDp06dGn369KkPWeYcUEKjoFw2Vu/evaNHjx7x7rvvZnc205L8+/XUU0+Nc845J+/IUl9jW+g5lfklgnKZIs4JECBAgAABAgQIECBAgAABAgQIECBAgAABAukSEJQrsl5bb7Zi7PPtVaNTx4Ss+eOV0VPqX+u5/VYrNRlczI5ygnJNyBbpxI5yi8TnYgIECCySQKEASmsF5U455ZT6e0h2lUt2CEuCbskrG+fMKf712RtssEEkr/fcfffdC3rccMMNceihhxYcU2ynoFy21KabbhrPPvtsdsd/WpL6HnbYYQUDbl26dIlkV8Af/ehHeee58MIL46STTsrbX0qHoFy2VvJ35Nprr63/e5gEHJMAZDHh0mRXwWuuuSZ22GGH7En/0/LGG2/E+uuvHwsWLMg7JrOj0HMqc6ygXKaIcwIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAugQE5UqoV5/e3WP/PftG/9WWyHvVzJkL4t5HPo2HHv9X7PjNXrHXbn2ajD3/0jHx0fh/v/60SUejk43WWzYO/+4ajVrK8LHuNbFnXvh6TJz872BA3YYccd7PNojk1bINx3V/fD9eeHVyw2n9zyXr+kf+dP0mAcFRl78Z73/U9JWqvVdcLE4/rtErWuu+b8QFr8ekKcUHEZp8cZlOVqx7deyZx9etq7h8Y9Hf+otfj65/5W7RFxhIgACBdihQKIDS2kG5xvzJqzofffTReO+99+L999+PDz744OufybjktY3Jn379+sV2220Xe++9d95d5Brm/eijj2LjjTeOL774oqFpkX4KyuXm+8c//hG77LJL7s661tGjR9eH4J5//vkmr9xNAnJDhgyJSy65JJLX4+Y7Zs6cGYMGDYqknuU4BOWyFZOg3HXXXfd1R/L379Zbb42xY8d+/Xfx008/rX8t7sorrxxJQPKAAw6InXfeOZI65jtqa2vrxzz44IP5huRsL/ScyrxAUC5TxDkBAgQIECBAgAABAgQIECBAgAABAgQIECBAIF0CSXyoLtLkKEUgCV+ts9bS0XOZrnX/4Tzqdq2ojfl1f8ZPmBmvjZka8+cjLcXTWAIECBCoToFCAZS2DMqVW3v27Nmx5ZZbxksvvVS2qQXlclOuvfba8cwzz8TSSy+de8B/WpMdxcaMGVMfhkx2IltnnXWia9euBa9JOo844oi4+uqrmx1X7ABBuWypzKBc9oiFazn++OPrdwss9epCz6nMuQTlMkWcEyBAgAABAgQIECBAgAABAgQIECBAgAABAgTSJSAol656WS0BAgQIEEiNQKEASjUF5Q4++OC46aabylqXAw88MP7whz80O2eyK96AAQOaHVdNA3baaae45557onPn/+6KW477u+222+p3LivHXA1zPPfcc7HJJps0nOb9efTRR0fyGtL2cLREUO7ss8+OESNGLBRfoedU5oSVFJQr1vHNN9+MwYMHZ96KcwIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAuxQQlGuXZXfTBAgQIECg5QUKBVCqISg3a9asOPnkk1sk4PS73/0ujjrqqGaLdOONN8YhhxzS7LhqG3D44YfHFVdcUbaw3PXXXx/f//73Y968eWWj6tGjR0yZMqXg60KTL0teGZq8trecOxKW7SZaYKJiA17FfPX8+fNj5MiR8fOf/7yY4TnHFHpOZV5QSUG5q666Kv7f//t/mUvMOk92SEx2SnQQIECAAAECBAgQIECAAAECBAgQIECAAAECBAhECMr5LSBAgAABAgRaRKBQAKU1gnLDhg2Lc845J/r06VP2+3v66adj+PDh8fbbb5d97mTC5LWhgwYNKjh3EupKXkX6/vvvFxxXrZ3f+MY3Igm4rbvuugt9izNnzoyzzjorfvWrXy30HPku3HnnneO+++7L1/11+1133RV777331+fV/mHXXXet3xFwUe/zhRdeqA+AvfLKK4s0VaHnVObElRSUe/fdd2ONNdbIXGKT87lz58aaa64ZH330UZN2JwQIECBAgAABAgQIECBAgAABAgQIECBAgACB9iogKNdeK+++CRAgQIBACwsUCqC0RlCu4fYGDhwYO+ywQ/2f7bbbLnr27NnQVdLPJHRy5513xmWXXRZPPvlkSdeWMrhXr17x2WefNXvJlVdeGT/4wQ+aHVfNA7p16xY/+clPIglFrrPOOkXfahKQu/baa+t3I5swYULR15Uy8Nxzz41TTz214CU1NTWx/vrrx+jRowuOq6bO7t27xy677BJ77rln7LHHHiX9fUx2kHvkkUfi1ltvjRtuuCEWLFiwyDSFnlOZk1dKUG7VVVctKvyWvM43ea2vgwABAgQIECBAgAABAgQIECBAgAABAgQIECBA4N8CgnJ+EwgQIECAAIEWESgUQMkXlKupWRBvffJ63vVcc+9FMXrcy3n77z/3tbx9SUfHjh1jgw02iMGDB8dKK62U9Wf8+PGx1lprxdSpU+tfmzlx4sT6V2I+++yz8eKLL0YSsGrp44ADDqgPAhX6njlz5tTvFPXxxx8XGtau+pKa7rXXXvW7bPXu3TuSwGFSz2TXraSOyZ+33norHnjggfqgY2LYksczzzwTQ4cOLfgVSeDrwAMPLDimmjs7d+4c3/zmN2PTTTeNVVZZ5es/yW6JXbt2jaRGSZAxCY6++uqr8Ze//CUmTZpUVpLM59ShOx8d6/ffOOd3LLn40rHqCv2y+mbOmR57nbVlVnu+huaeU/mua2g/+OCD64OCDee5fiavhk5+94sJ3ea6XhsBAgQIECBAgAABAgQIECBAgAABAgQIECBAoBoFBOWqsaruiQABAgQIVIBAZgCl8ZLyBeUaj8n1+ec3HRtPv/lIrq76tkUNoOSduBU7kp3ijjjiiILfeMkll8QxxxxTcIzOthNYaqmlYvLkydGpU6e8i0h2Q0vCfWPHjs07RkfLC2Q+p844cFRste6OJX1xawflklcOH3LIIQXXOGrUqDjxxBMLjtFJgAABAgQIECBAgAABAgQIECBAgAABAgQIEGhvAoJy7a3i7pcAAQIECLSSQGYApfHXCso11mj6+Z133okBAwY0bWx0luxql+wU1VKvDG30VT4upMDuu+8ef/vb3wpefd1118Vhhx1WcIzOlhfIfE6lISj34YcfRt++ffPiTJ8+Pfr161e/i2LeQToIECBAgAABAgQIECBAgAABAgQIECBAgAABAu1QQFCuHRbdLRMgQIAAgdYQyAygNP5OQbnGGv/93KdPn2judaoXXHBBnHzyyf+9yKeKE7jwwgvjhBNOyLuu5NWiAwcOjHHjxuUdo6N1BDKfU5UelOvfv3+89957BXFGjhwZp59+esExOgkQIECAAAECBAgQIECAAAECBAgQIECAAAEC7VFAUK49Vt09EyBAgACBVhDY6dT1okPyL40cx+aDto2fD7s4R0/hpkKvXq2tjXjgvNcKT1DhvcOGDYsbb7wx7yqnTZsWSVBm4sSJecfoaHuBF198MTbaaKO8C7n88svjqKOOytuvo/UEMp9Tx37njNht031KWkApr15d1OfU4YcfHldffXXe9U2dOrV+N7nkp4MAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKCpgKBcUw9nBAgQIECAQJkEdjqlLijXMfdkHeoSdGv0Xju6dOqSe0Ce1o8njovps77K2VtbUxeUOz/dQblrr702hg8fnvP+ksazzz47RowYkbdfR9sLLLvssvVBxo4dc//yz549u/7VuuPHj2/7xVpBZD6nunVZLPr3GliSzIKaBTF2/BtFXbOoz6mbb745DjrooLzfdcYZZ8Qvf/nLvP06CBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQLtWUBQrj1X370TIECAAIEWFNjxpCHRsXOeLeVa4Htr5tfGgxe83gIzt96UH3zwQay++uo5v3DKlCn1u8nZKSonT8U07rnnnnH33XfnXc9FF10Uxx13XN5+Ha0rkLbnVBKwXHnllXMiJTtNJjtOJjtPOggQIECAAAECBAgQIECAAAECBAgQIECAAAECBLIFBOWyTbQQIECAAAECZRDY/oR1o3PX/+6q1bFLx+i+bNfo1qNLdFmsU3SqO+/Yua7/P/8aqZlfEwvm1cS82Qtizox5MXPK3KipOy/2mD+nJh7+9ehih1fcuH79+sX777+fd12nn356jBw5Mm+/jsoQuPjii+OYY47JuZgZM2bUB5k+//zznP0aW18gTc+ptdZaK9566628SCeffHJccMEFeft1ECBAgAABAgQIECBAgAABAgQIECBAgAABAgTau4CgXHv/DXD/BAgQIECghQS2OXpwdFuic3Tu1imWXrl7XUiuW95XseZaQvKKwplT5sSXn86M+XMW5BrSpG3O9Pnx6CVjmrSl6eSwww6La665JueSv/jii/qA1fTp03P2a6wcgddeey2GDBmSc0HnnXdenHbaaTn7NLaNQJqeU0ceeWRcfvnlOaEmTJgQa6yxRsycOTNnv0YCBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGv93BBQYAAAQIECBAor8AWR6wVK6+zTCy9So+SAnKZq0gCc1+OnxFf/WtWZleT8+kT58RTV73dpC1NJ5dddln88Ic/zLnkE088MUaNGpWzT2PlCHTv3j2+/PLL6Ny5c9aivvrqq0h2DZw8eXJWn4a2E0jTc+rqq6+Oww8/PCdWsovhJZdckrNPIwECBAgQIECAAAECBAgQIECAAAECBAgQIECAwL8F7CjnN4EAAQIECBAou0CHjh3i2yM3ihUHLl22uWdMmhOTxtXtqFZbm3POyR9Ojxduyf/q0pwXVVDj8ssvH1tttVXU1NTU3WLt13+S80ceeSRmzSocFKygW2nXSxk6dGj06tWrvn6Na5ns+PXSSy+1a5tKu/m0PadWXHHF2Hzzzb9+NiTPiYbfsYcffjjmzJlTacTWQ4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBCoKAFBuYoqh8UQIECAAIHqEBjyP31jnd36xBIrLFbWG6oPy30wLeecn7wyKcb8Y3zOPo0ECBDIFPCcyhRxToAAAQIECBAgQIAAAQIECBAgQIAAAQIECBCobgFBuequr7sjQIAAAQKtLrD6pivEwB16R4/lFovl+i1R9u+f+nHu17C+8fdPYvxrXmtZdnATEqhCAc+pKiyqWyJAgAABAgQIECBAgAABAgQIECBAgAABAgQINCMgKNcMkG4CBAgQIECgeIHFl+kaW35/rejYqUN07NwhVllvuejQsfjrixlZWxPx2RtTYv6cBV8Pr1lQG49dOibmzvxv29edPhAgQKCRgOdUIwwfCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQLtSEBQrh0V260SIECAAIGWFkheZdh7nWW+/ppkR7lkZ7lyH5mvYP109JQY/X8fl/trzEeAQBUKeE5VYVHdEgECBAgQIECAAAECBAgQIECAAAECBAgQIECgCAFBuSKQDCFAgAABAgSaF+jao3Ns/aNB9bvJNYzu3LVj9F532bpd5ZJ/cpTvSHaVG//65KiZVxM182vjySvfjllfzi3fF5iJAIGqFPCcqsqyuikCBAgQIECAAAECBAgQIECAAAECBAgQIECAQFECgnJFMRlEgAABAgQINCewygY9Y51d+2QNW3LFxWPZvj2y2he1YdK46TFj4ux464FP46MXJi7qdK4nQKAdCHhOtYMiu0UCBAgQIECAAAECBAgQIECAAAECBAgQIECAQB4BQbk8MJoJECBAgACB0gQG79Yn+qzfM+dFy62+ZPRYvlvOvoVtnF4Xkktetzr6b165urCGriPQ3gQ8p9pbxd0vAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQOC/AoJy/7XwiQABAgQIEFgEgY0P6h89+y6Re4a6f3Est1p5w3L/evvL+NvpL0VtTW3u79RKgACBDAHPqQwQpwQIECBAgAABAgQIECBAgAABAgQIECBAgACBdiQgKNeOiu1WCRAgQIBASwpsccRasUQzu8Ylr2Fdpk/36NAx+SfIwh1JMG7qJzPjszFT46mr3l64SVxFgEC7FPCcapdld9MECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgXoBQTm/CAQIECBAgEBZBLY9enB0XaJzs3N17toxBg5aMzZbe9uIUvJydRvHPf3mP+Odt96N+XNrYu70+fHIJWOa/T4DCBAg0CDQ3HNq6Npbx8rL9Y2OnTrE4kt3ja7d655pzTyn5tfUPYve/0v9V9TWRMycMju+HD+zrM+p733ve7H88ss33EZRP//1r3/FrbfeWtTYhR109NFHR6dOnUq6/K233op77723pGsWZnC/fv1i2223zbr0tttui5kzZ2a1ayBAgAABAgQIECBAgAABAgQIECBAgAABAgSqX0BQrvpr7A4JECBAgECrCGx/wrqRhOCKObYYvH2c+b3fFDO0yZif33RsXVjukfq2JCz38KjRTfqdECBAoJBAc8+pMw4cFVutu2OhKbL6Zs2bEYdf962YM2NezJo6NxbMq0vL/eco13PqtddeiyFDhjRMW9TPF154ITbZZJOixi7soNmzZ0e3bt1KuvyWW26Jgw46qKRrFmbwfvvtF0koLvM488wz4xe/+EVms3MCBAgQIECAAAECBAgQIECAAAECBAgQIECgHQgIyrWDIrtFAgQIECDQGgI7njQkOnZuZuul/yykHEG5mvm18eAFr7fGrfkOAgSqRKC559TCBOVmzpkee521ZU6hcj2nBOVy8hZsTHa6GzNmTAwcOLDJuBkzZtS3ffrpp03anRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQLVLyAoV/01docECBAgQKBVBHY6Zb3oUNyGclGOoFzyisMHzn+tVe7NlxAgUB0CzT2nyh2UK9dzSlBu4X7/kp3rbr755qyLr7/++hg+fHhWuwYCBAgQIECAAAECBAgQIECAAAECBAgQIECgugUE5aq7vu6OAAECBAi0msBOp9YF5YrbUG6hg3Jn3nhMPPPWo/X3VFtbF5Q7rzxBuc6dO8eqq64affv2jZqampg8eXL9n4kTJ8a8efNazdAXESDQsgLNPafKHpQr03NKUG7hfi86duwYo0ePjkGDBjWZIHnOb7zxxvHyyy83aXdCgAABAgQIECBAgAABAgQIECBAgAABAgQIVLeAoFx119fdESBAgACBVhPY+bT1iv6uQjvK/emJG3POM3vurPjjI9fE3Plzvu6//9zSg3L9+vWLfffdtz44kXxeffXVo0+fPpG8pi/zmDt3brzwwgvxxBNPxN/+9rd4/PHHM4c4T4HAfvvtF71798670htvvDGmTJmSt19HeQSSMOr//u//lmeyHLO8+OKL9X9Xc3R93dTcc2rHDb8d/Xs3fVVnw8W9e/apD/k2nDf8LPTq1WTMwjynGuZu+HnCCSfk/R3eZJNNYuutt24Y+vXP5NmV9LXkcd5550USNM517LzzzjFkyJCsrltuuSWSnd5a69h///3jj3/8Y9bXPfLII7HddttltWsgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEqldAUK56a+vOCBAgQIBAqwo0F0BpvJh8Qbnps6fF3r/YqvHQgp+LDaAss8wy9eG4YcOGxVZbbVW3813yT6DSj2T3oVGjRkUS9KhNtrRzVLxAUu/HHnusYM3XXnvtePvttyv+XtK+wB122CEefPDBFruNCy+8ME466aSC85fynMqcaPNB28bPh12c2RytEZTL+tJGDSeeeGJccMEFjVr+/bE1gnJZX9qo4corr4wjjjiiUcu/P7Z2UC553ic78q277rpZa9lrr73i7rvvzmrXQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECFSngKBcddbVXREgQIAAgVYXKCWA0lpBuW222SZ+9KMfxbe//e1YbLHFymZy3333xfDhw+Ozzz4r25wmKr9A165d45VXXsl67WLmNwnKZYq0zLmgXMu4Cso177r33nvHnXfemTXw3XffjcGDB3vFdpaMBgIECBAgQIAAAQIECBAgQIAAAQIECBAgUJ0CgnLVWVd3RYAAAQIEWl2gEoNy//d//xd77LFHi1hMnDgxdtlll0he9+ioTIERI0bEWWed1eziBOWaJSrLAEG5sjBmTSIol0WS1ZDsKpfsGrnmmmtm9SWvZr7jjjuy2jUQIECAAAECBAgQIECAAAECBAgQIECAAAEC1ScgKFd9NXVHBAgQIECgTQTaW1AuQU7Cct/85jfjrbfeahNzX5pfIAm/JbvJdevWLf+g//QIyjVLVJYBgnJlYcyaRFAuiyRnwxlnnBG/+MUvsvoefvjhSH43HQQIECBAgAABAgQIECBAgAABAgQIECBAgED1CwjKVX+N3SEBAgQIEGgVgbQG5ebPnx9vvPFGjB8/vj74ttRSS8Vaa60Va6yxRiSv7mzu+OCDD2KdddaJWbNmNTdUfysJJLtHPfroo/UhxmK+UlCuGKVFH9PSQblzzjknfvaznxVcaCnPqcyJNh+0bfx82MWZzTFzzvTY66wts9obGu4/97WGjy3yU1CuONb+/fvHe++9l3Nw8swfO3Zszj6NBAgQIECAAAECBAgQIECAAAECBAgQIECAQPUICMpVTy3dCQECBAgQaFOBUgIoWwzePs783m+y1jt99rTY+xdbZbXna2gugJLv1av/+te/4pZbbokHH3wwHn/88Zg2bVrWV3Tu3Dl23XXXuPjii6Nfv35Z/Y0bzjzzzJw7FTUe43PrCRxxxBFx5ZVXFv2FgnJFUy3SwEJBucMPP3yR5q6trY0777wz59/lxhOX8pxqfF3yWVAuU6TwefJ3MPm7mHkkz96DDjoos7lVzp944onYcsvsUONvfvObOP7441tlDb6EAAECBAgQIECAAAECBAgQIECAAAECBAgQaDsBQbm2s/fNBAgQIECgqgRKCaC0VVAuecXeFVdcEXfffXfMmzevKP/FF188klf2nXbaaXnHz5w5M1ZdddWYPHly3jE6Wkegd+/eMWbMmFhmmWWK/kJBuaKpFmlgvqBcsqtjly5dFmnuYi8u5TmVOaegXKZI4fNKDMr94Ac/qP/fgMyVJ8/uVVZZJWbPnp3Z5ZwAAQIECBAgQIAAAQIECBAgQIAAAQIECBCoIgFBuSoqplshQIAAAQJtKVBKAKW1g3JPP/10nHLKKfW7xy2s0c0331xwF6RDDjkkbrzxxoWd3nVlErj99ttj3333bTLb559/Hk8++WTstddeTdobTgTlGiRa9qegXMv4evVq8a7LLrtsTJgwIedrtQ899NC44YYbip/MSAIECBAgQIAAAQIECBAgQIAAAQIECBAgQCB1AoJyqSuZBRMgQIAAgcoUqMSgXBLMWXLJJet3kFtUteWWWy7efPPNWGGFFXJOddddd8Xee++ds09j6wh8+9vfjr/+9a9ZXzZ8+PDYeuutI/mZ6xCUy6VS/jZBufKbJjMKypXmmjyrc4Vmn3nmmdh8881Lm8xoAgQIECBAgAABAgQIECBAgAABAgQIECBAIFUCgnKpKpfFEiBAgACByhWoxKBcubVOOOGEuPDCC3NO+/7778caa6yRs09jywskgcg33nij/hW4jb8t2U1wyy23jGuuuUZQrjFMG3wWlGsZdEG50lyHDRuWc/fP2traWHHFFWPixImlTWg0AQIECBAgQIAAAQIECBAgQIAAAQIECBAgkBoBQbnUlMpCCRAgQIBAZQu0h6Dc9ttvHw899FDOQsycOTN69OiRs09jywtcfPHFccwxxzT5opqamth4443j5ZdfjmuvvVZQrolO658IyrWMuaBcaa4DBgyId955J+dFya6gyY5zDgIECBAgQIAAAQIECBAgQIAAAQIECBAgQKA6BQTlqrOu7ooAAQIECLS6QHsIyvXq1Ss+++yzvLZLLLFEzJgxI2+/jpYRGDp0aDz11FPRsWPHJl9w+eWXx1FHHVXfJijXhKZNTgTlWoZdUK5012TXuOR12pnHb3/72zj22GMzm50TIECAAAECBAgQIECAAAECBAgQIECAAAECVSIgKFclhXQbBAgQIECgrQXaQ1AuMZ4zZ0507do1J3eyo1yys5yj9QQ6d+4cL774Yqy33npNvjQJwgwcODCmTJlS3y4o14SnTU4E5VqGXVCudNd77rkndt1116wLX3311dhggw2y2jUQIECAAAECBAgQIECAAAECBAgQIECAAAEC1SEgKFcddXQXBAgQIECgzQXaQ1Cue/fueXeMmzx5cs4ditq8MFW+gFNPPTXOPffcrLs84ogj4uqrr/66XVDua4o2+yAo1zL0gnKlu44YMSLOOuusrAtra2vrn+MNAdusARoIECBAgAABAgQIECBAgAABAgQIECBAgACBVAsIyqW6fBZPgAABAgQqR6A9BOXWXXfdeP3113Oiv/LKK7Hhhhvm7NPYMgIDBgyI1157LRZffPEmX/Dcc8/FZpttFknopeEQlGuQaLufgnItYy8oV7rrt771rbj33ntzXrjnnnvGX//615x9GgkQIECAAAECBAgQIECAAAECBAgQIECAAIF0CwjKpbt+Vk+AAAECBCpGoD0E5X72s5/FL3/5y5zmN9xwQxx66KE5+zS2jMCDDz4YSfiq8VFTU1Mfknv++ecbN4egXBOONjnJF5RLFrPddtvFzjvvHOuvv36stNJKscIKK0SHDh1i0qRJkbxG9/PPP49nn302HnrooXjjjTcWev2lPKcyv2TzQdvGz4ddnNkcM+dMj73O2jKrvaHh/nNfa/jYIj8F5UpnXXbZZet/t5Lfscxj1KhRkZg6CBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgeoTEJSrvpq6IwIECBAg0CYCpQRQthi8fZz5vd9krXP67Gmx9y+2ymrP19DSAZTM7012k0t2lct17LbbbvGPf/wjV5e2FhA45JBD4vrrr8+a+aqrrorvf//7We2Cclkkrd5QKChXymLGjx8fF198cVx22WV5X4Wcb75SnlOZcwjKZYoUPr/yyisjeQVy5nHLLbfEQQcdlNnc6ueffPJJrLLKKlnf+6c//Sn22WefrHYNBAgQIECAAAECBAgQIECAAAECBAgQIECAQPoFBOXSX0N3QIAAAQIEKkKglABKGoNye+21V9x11105rZMdr3r37h3z58/P2a+xvALJbmNvvvlmLLfcck0mnjx5cgwcOLB+p6gmHXUngnKZIq1/Xq6gXMPKv/jii0h2eUzCkcUepTynMucUlMsUKXxe6UG5V199NdZbb72sm3jsscdim222yWrXQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECKRfQFAu/TV0BwQIECBAoCIESgmgpC0ot9hii9UHs1ZfffWc1iNHjozTTz89Z5/G8gvcdNNN8b3vfS9r4h/+8IdxxRVXZLUnDYJyOVlatbHcQbmGxSdBuR//+Mcxd+7chqa8P0t5TmVOIiiXKVL4vNKDcv/85z9j2223zbqJJIQ7ePDgrHYNBAgQIECAAAECBAgQIECAAAECBAgQIECAQPoFBOXSX0N3QIAAAQIEKkKglABK2oJyv/jFL+KMM87I6fzpp5/W72I2Y8aMnP0ayyvwrW99K+69996sSV966aXYZJNNoqamJqsvaRCUy8nSqo0tFZRLbuLhhx+OXXbZJebNm1fwnkp5TmVOJCiXKVL4vNKDcnfeeWfsvffeWTeR7BCa7FrpIECAAAECBAgQIECAAAECBAgQIECAAAECBKpPQFCu+mrqjggQIECAQJsIlBJASVNQbrvttosHHnggOnXqlNP14IMPjmSHM0fLC3Tv3j1Gjx4d/fr1a/JltbW1scUWW8QzzzzTpL3xiaBcY422+dySQbnkji699NI4+uijC95cKc+pzIkE5TJFCp9XelAu3/qSsG3Xrl1jwYIFhW9QLwECBAgQIECAAAECBAgQIECAAAECBAgQIJA6AUG51JXMggkQIECAQGUKlBJASUtQrlevXvHyyy9H8jPXkQTokh3OkqCWo+UFfvWrX8VJJ52U9UXXXXddHHbYYVntjRsE5RprtM3nzKDc9OnT44477ojnnnsuPvroo/o/H3/8cUybNi369u1bv1PjWmutFUOGDIlhw4ZF8grk5o4DDzwwbr311rzDSnlOZU4iKJcpUvg8XxDtlltuiYMOOqjwxa3Qe95558Upp5yS85tWWmml+Pzzz3P2aSRAgAABAgQIECBAgAABAgQIECBAgAABAgTSKyAol97aWTkBAgQIEKgogVICKGkIyiU7yD344IOx7bbb5nROAj0bbbRRJK/pc7S8wIYbbhjPP/981s5+U6dOrQ9UffHFFwUXIShXkKdVOpO/S8krUh999NG4/vrrI3n1ZbGvLO7fv39cdNFF8e1vf7vgWseNG1f/+5DvFaylPKcyv0hQLlOk8HmlB+WSkFwSlst1DB48ON58881cXdoIECBAgAABAgQIECBAgAABAgQIECBAgACBFAsIyqW4eJZOgAABAgQqSaCUAEoagnKXXXZZ/PCHP8xJPHfu3Nh6663j2WefzdmvsbwCSWgxsf7GN76RNXHyqs3klZvNHYJyzQm1Tv8KK6wQzYUaC61k9913jxtuuCGWW265vMMOP/zwSOqd6yjlOZV5vaBcpkjh80oPyv30pz+NkSNH5ryJZCfDsWPH5uzTSIAAAQIECBAgQIAAAQIECBAgQIAAAQIECKRXQFAuvbWzcgIECBAgUFECpQRQKj0od9ppp8U555yT1/eoo46Kyy+/PG+/jvIKHHfccfHrX/86a9JXX321Pjy3YMGCrL7MBkG5TJH0nh9wwAEFX6/6yiuvRLIDYa6jlOdU5vWCcpkihc8rPSh34YUXxgknnJDzJnr27BlTpkzJ2aeRAAECBAgQIECAAAECBAgQIECAAAECBAgQSK+AoFx6a2flBAgQIECgogRKCaBUclDu4IMPrt+xKh9uEtjKF67Id432hRdYffXVY/To0dGjR4+sSbbaaqt48skns9pzNQjK5VJJb9vdd98de+65Z84bqK2tjRVXXDHna5FLeU5lTi4olylS+LzSg3L5ngnz58+Prl27RvJ75CBAgAABAgQIECBAgAABAgQIECBAgAABAgSqS0BQrrrq6W4IECBAgECbCZQSQKnUoNwee+wRf/7zn6Nz5845HW+77bb47ne/K0CRU6dlGu+5557Yddddsya/6aabIgk1FnvkC8Uk16+99trx9ttvFzuVcRUg0Lt37xgzZkwss8wyOVez3377xR133JHVV8pzKvNiQblMkcLnlR6U+8tf/hL/8z//k3UTEyZMiOT3y0GAAAECBAgQIECAAAECBAgQIECAAAECBAhUn4CgXPXV1B0RIECAAIE2ESglgFKJQblkd7L7778/Fl988Zx+Dz/8cOy2224xZ86cnP0ayy+Q7xWbX331Vay11lqRBFqKPQTlipVKz7g//vGPsf/+++dc8KhRo+LEE0/M6ivlOZV5saBcpkjh80oPyj3++OORPPczj9dffz3WW2+9zGbnBAgQIECAAAECBAgQIECAAAECBAgQIECAQBUICMpVQRHdAgECBAgQqASBUgIolRaUS0IRjz76aN7dqV588cXYbrvtYtq0aZVA3S7W0KVLl/jkk0/qX6GZecPHHXdcXHTRRZnNBc8F5QrypLJzxIgRcdZZZ+Vc+4033hiHHHJIVl8pz6nMiwXlMkUKn1d6UC7ZkXDQoEFZN5GEonfYYYesdg0ECBAgQIAAAQIECBAgQIAAAQIECBAgQIBA+gUE5dJfQ3dAgAABAgQqQqCUAEolBeX69+8fTzzxRN5X7Y0dO7Z+16EvvviiIpzbyyKWXHLJSHaOy3X87ne/i/nz5+fqytu20047xeDBg3P2J6GqKVOmZPUl3/POO+9ktWuoDIF99tkn5+tVk9Ulr+zdfffdsxZaynMq82JBuUyRwueVHpSbOHFiLLfcclk3kbxiO9nN0kGAAAECBAgQIECAAAECBAgQIECAAAECBAhUn4CgXPXV1B0RIECAAIE2ESglgFIpQbmVV165PiTXr1+/nGbjx4+PLbfcMj788MOc/RpbTqBQUK7lvrXpzDvuuGM89NBDTRudVYzAuuuuG8lrMnMdzzzzTGy++eZZXaU8pzIvFpTLFCl8XslBuVVXXTU++uijnDdw9tlnR7JboYMAAQIECBAgQIAAAQIECBAgQIAAAQIECBCoPgFBueqrqTsiQIAAAQJtIlBKAKUSgnI9e/aMxx57LNZZZ52cXsluQ1tvvXW8+eabOfs1tqyAoFzL+lbD7Mnf3dGjR+e8lRdeeCE22WSTrL5SnlOZFwvKZYoUPq/koNy+++4bt99+e84bSHaffPDBB3P2aSRAgAABAgQIECBAgAABAgQIECBAgAABAgTSLSAol+76WT0BAgQIEKgYgVICKG0dlFtiiSXqdwrbdNNNc/pNmzYttt9++0jCNo62ERCUaxv3NH3rXnvtFXfddVfOJSdBpyTwlHmU8pzKvFZQLlOk8HklB+UuvPDCOOGEE7JuYN68ebHMMsvEzJkzs/o0ECBAgAABAgQIECBAgAABAgQIECBAgAABAukXEJRLfw3dAQECBAgQqAiBUgIobRmU69atW9xzzz31QbhccLNmzYpddtmlfre5XP3aWkdAUK51nNP8Laeddlqcc845OW/h+uuvj+HDh2f1lfKcyrxYUC5TpPB5JQflHn/88dhqq62ybuDpp5+OLbbYIqtdAwECBAgQIECAAAECBAgQIECAAAECBAgQIFAdAoJy1VFHd0GAAAECBNpcoJQASlsF5Tp37hx33nln7Lnnnjm9kt2EvvOd79QH6XIOaKXGoUOHximnnNLst02YMCGOOuqoZselcUBbB+WSwORGG20Ub7311kLznX322Xlf7dt40quvvrrNf+caryctn2+88cYYNmxYzuWOHDkyTj/99Ky+Up5TmRcLymWKFD6v1KBc8r8DX331VSy++OJZN3DeeedFEsB0ECBAgAABAgQIECBAgAABAgQIECBAgAABAtUpIChXnXV1VwQIECBAoNUFSgmgtEVQrmPHjpEEaw466KCcNjU1NXHAAQfEHXfckbO/NRuTsN6f//znZr/yvffeiwEDBjQ7Lq0DevbsWbalX3LJJXHggQfmnC/ZQertt99u0jd37tyYPn16k7ZSTx555JHYZpttmr3s6KOPjksvvbTZcQb8VyD53UhCjCussMJ/Gxt9+u53vxt//OMfG7X8+2Mpz6nMiwXlMkUKn1dqUC4JwL744os5F7/rrrvGvffem7NPIwECBAgQIECAAAECBAgQIECAAAECBAgQIJB+AUG59NfQHRAgQIAAgYoQKCWA0hZBucsvvzyOPPLInFa1tbVx+OGHx3XXXZezv7UbBeXKL37ttdfmfBVn8k1rr712VlCuHCsQlCuHYu45CtVzwYIF9QG6KVOmZF1cynMq82JBuUyRwueVGpQ75phj4uKLL85afPJ7s+yyy8a0adOy+jQQIECAAAECBAgQIECAAAECBAgQIECAAAEC1SEgKFcddXQXBAgQIECgzQVKCaC0dlDu/PPPj5NPPjmv0bHHHhu//e1v8/a3doegXPnFCwWrBOXK792SM26//fbx0EMP5f2Kxx9/PLbeeuuc/aU8pzInEJTLFCl8XqlBuSeffDKSXSQzj2QnuWRHOQcBAgQIECBAgAABAgQIECBApz8yHgAAQABJREFUgAABAgQIECBQvQKCctVbW3dGgAABAgRaVaCUAEprBuV+9rOfxS9/+cu8FqeffnqMHDkyb39bdAjKlV9dUK78psXOuPnmm8ftt98e119/fVx99dXx4YcfFntp1rjNNtus/pWqq622WlZfQ0PyeuVbbrml4bTJz1KeU00urDsRlMsUKXxeiUG5/v37R/LK6lzHnnvuGX/9619zdWkjQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEqkRAUK5KCuk2CBAgQIBAWwuUEkBpraDc0UcfXXCnuPPOOy9OO+20tqbL+n5BuSySRW4QlFtkwoWeINm9K9nFKzlqamoi2bnr97//ffz973+P5HWXxRzLL798JDtDDh8+PDp0SP5PmNzHq6++GhtuuGEkr1POdZTynMq8XlAuU6TweSUG5UaMGBFnnXVW1sI//vjj6NevX9G/j1kTaCBAgAABAgQIECBAgAABAgQIECBAgAABAgRSISAol4oyWSQBAgQIEKh8gVICKK0RlFt66aVjypQpBUM1r7/+et5AzcKIn3HGGWXZkUhQbmH0C18jKFfYpyV7GwflGn/PJ598Eg8++GB88MEHTf589tlnsfLKK8fAgQPr/ySvxj344INj2WWXbXx51uckhLfzzjsXfC1rKc+pzC8QlMsUKXxeiUG5sWPHxpprrpm18CRAd/bZZ2e1ayBAgAABAgQIECBAgAABAgQIECBAgAABAgSqS0BQrrrq6W4IECBAgECbCZQSQGmNoFzPnj1j0qRJrepx6KGHxg033LDI3ykot8iEWRMIymWRtFpDvqBcuRdw0kknxYUXXlhw2lKeU5kTCcplihQ+r7Sg3NChQ+OZZ57JWvT8+fOjb9++kQQ0HQQIECBAgAABAgQIECBAgAABAgQIECBAgEB1CwjKVXd93R0BAgQIEGg1gVICKIJyhcty4IEHxh/+8IfCg+p633vvvRgwYECz4wyIaIug3HPPPRebbLJJs/zJK4IvvfTSZseldUBrBOUuv/zyOOqoo5olKuU5lTmZoFymSOHzSgvKXXLJJfHjH/84a9F/+tOfYp999slq10CAAAECBAgQIECAAAECBAgQIECAAAECBAhUn4CgXPXV1B0RIECAAIE2ESglgCIoV7hEv/vd74oK/dx4441xyCGHFJ5Mb71AawflevToUf/q3y5duhSsQG1tbWy88cbx0ksvFRyX5s6WDMrNnTs3jj/++Ej+zhRzlPKcypxPUC5TpPB5JQXlFltssfj4449j+eWXz1r0TjvtVP8K4KwODQQIECBAgAABAgQIECBAgAABAgQIECBAgEDVCQjKVV1J3RABAgQIEGgbgVICKIJyhWs0ZsyYGDRoUMFB8+bNi7XXXjvef//9guN0/lugtYNyO++8c9x3333N8t91112x9957NzsuzQM222yzePrpp8t+C88//3z9DmHJzn3FHqU8pzLnFJTLFCl8XklBuWOPPTYuuuiirAU/++yzsfnmm0cSWHUQIECAAAECBAgQIECAAAECBAgQIECAAAEC1S8gKFf9NXaHBAgQIECgVQRKCaC0RlAu2UHo888/jyWXXLJV7j/5kl133TXuvffeRfq+Xr16xWeffdbsHEkI5Qc/+EGz4wz4t8C5554bp556ahZHEjhcZZVV4osvvsjqW5SGfN/XeM6amppYf/31Y/To0Y2bq/LzaqutFkl4MNm9a4cddoiePXsu1H3Onz8/HnroofjVr34VDz/8cMlzlPKcypxcUC5TpPB5pQTlFl988fpAcfJszTy23HLLeOqppzKbnRMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQJVKiAoV6WFdVsECBAgQKC1BUoJoOQLytXULIi3Pnk979KvufeiGD3u5a/77z/3ta8/V8uHAw44IG699daCtzNnzpxYc801618lWHCgzjYTeOaZZ2Lo0KEFvz+p84EHHlhwTDV2duzYsf51s5tuumkk4aXkz0orrVT/MwnQderUKd544436AOPkyZNj0qRJMXbs2Hj00UfrQ03Tp09faJbmnlOH7nx0rN9/45zzL7n40rHqCv2y+mbOmR57nbVlVntDQzmeU8lrltdYY42GKZv8TIKeSRAx83jhhRdik002yWwu6/k///nP6Nq1a845k/Umdc08brnlljjooIMym1vs/Ljjjotf//rXWfPffvvtsf/++2e1ayBAgAABAgQIECBAgAABAgQIECBAgAABAgSqV0BQrnpr684IECBAgECrCjQXQGm8mHxBucZjcn3++U3HxtNvPvJ1VzkCKF9PViEf8u3C1Hh5l1xySRxzzDGNm3yuIIGllloqkoBXEvjKdyxYsCAGDx5cHwDLN0Z7+QWae06dceCo2GrdHUv64tYIyr322msxZMiQktbVGkG52bNnR7du3UpaV2sG5ZLd5D744IOswF4SNk5eXT1u3LiS1m4wAQIECBAgQIAAAQIECBAgQIAAAQIECBAgkG4BQbl018/qCRAgQIBAxQjsdOp60SH5l0URRzmCcrW1EQ+cV307yr3zzjsxYMCAvIozZ86s31lqwoQJecfoaFuB3XffPf72t78VXMR1110Xhx12WMExOssv0NxzqtxBuXI9pwTlFu534fjjj49Ro0ZlXXz++efnfBVz1kANBAgQIECAAAECBAgQIECAAAECBAgQIECAQFUJCMpVVTndDAECBAgQaDuBnU6pC8p1LO77yxKUq6kLyp1fXUG5Pn36NPs61QsuuCBOPvnk4qCNahOBCy+8ME444YS83z1v3rwYOHCg3azyCrVcR3PPqbIH5cr0nBKUK/13onv37vW7ya244opNLv7888/rX1391VdfNWl3QoAAAQIECBAgQIAAAQIECBAgQIAAAQIECFS/gKBc9dfYHRIgQIAAgVYR2PGkIdGxc3Fbym0+aNv4+bCLS15X41ev1syvjQcveL3kOSr5gmHDhsWNN96Yd4nTpk2L/v37x8SJE/OO0dH2Ai+++GJstNFGeRdy+eWXx1FHHZW3X0fLCTT3nDr2O2fEbpvuU9ICCr16tVzPqX/+85+x7bbblrSu1nj16vjx42PllVcuaV2t9erVAw44IG699dastR155JHx+9//PqtdAwECBAgQIECAAAECBAgQIECAAAECBAgQIFD9AoJy1V9jd0iAAAECBFpFYPsT1o3OXYvbUq5D3Tta1+i9dnTp1KWktX08cVxMn/XvXYDmz62Jh0eNLun6Sh987bXXxvDhw/Mu8+yzz44RI0bk7dfR9gLLLrtsfZCxY8fcfxdmz55d/2rdJGDkaH2B5p5T3bosFv17DSxpYQtqFsTY8W/kvKZcz6mePXvW70KY80vyNE6fPj1Gj27ZZ2QSkuvbt2+eFeRuToK+7777bu7OMrYmfwc32WSTuleCNw1wP//887FgwYIyfpOpCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgbQICMqlpVLWSYAAAQIEKlxg26MHR9clOrfaKudOnx+PXDKm1b6vNb7ogw8+iNVXXz3nV02ZMqV+N7mpU6fm7NdYGQJ77rln3H333XkXc9FFF8Vxxx2Xt19Hywp4TrWsr9kJECBAgAABAgQIECBAgAABAgQIECBAgAABApUsIChXydWxNgIECBAgkCKBLY5YK5ZYvlurrXj6xDnx1FVvt9r3tfQX9evXL95///28X3P66afHyJEj8/brqAyBiy++OI455pici5kxY0Z92PHzzz/P2a+x5QU8p1re2DcQIECAAAECBAgQIECAAAECBAgQIECAAAECBCpVQFCuUitjXQQIECBAIGUCGx/UP3r2XaLVVj35o+nxwh/yB8tabSFl+qLDDjssrrnmmpyzffHFF/UBq+RVio7KFnjttddiyJAhORd53nnnxWmnnZazT2PrCHhOtY6zbyFAgAABAgQIECBAgAABAgQIECBAgAABAgQIVKKAoFwlVsWaCBAgQIBACgUG79Yn+qzfs9VW/skrk2PMPz5pte9r6S+67LLL4oc//GHOrznxxBNj1KhROfs0Vo5A9+7d48svv4zOnbNfQfzVV19Fsmvg5MmTK2fB7XAlnlPtsOhumQABAgQIECBAgAABAgQIECBAgAABAgQIECDwHwFBOb8KBAgQIECAQFkEVtmgZ6yza5+yzFXMJG/UheTG14XlquVYfvnlY6uttoqampqora39+k9y/sgjj8SsWbOq5Var+j6GDh0avXr1qq9f41pOmDAhXnrppaq+9zTcnOdUGqpkjQQIECBAgAABAgQIECBAgAABAgQIECBAgACBlhEQlGsZV7MSIECAAIF2J9C1R+fY+keDomOn5J8XLXvULKiNx373ZsydMb9lv8jsBAhUlYDnVFWV080QIECAAAECBAgQIECAAAECBAgQIECAAAECBEoSEJQrictgAgQIECBAoJDAkP/pG73XWabQkLL0ffbG1Hj9rx+VZS6TECDQvgQ8p9pXvd0tAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKBBQFCuQcJPAgQIECBAYJEFFl+ma2x5xFrRsXPL7SpXM782nrzq7Zg1de4ir9cEBAi0PwHPqfZXc3dMgAABAgQIECBAgAABAgQIECBAgAABAgQIEEgEBOX8HhAgQIAAAQJlFVh90xVi4A69yzpn48nGPvRZjHvui8ZNPhMgQKAkAc+pkrgMJkCAAAECBAgQIECAAAECBAgQIECAAAECBAhUhYCgXFWU0U0QIECAAIHKEmipVxt65Wpl1dlqCKRZwHMqzdWzdgIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBA6QKCcqWbuYIAAQIECBBoRqBDxw6x7h6rRu91lmlmZPHdSUhu9N8+jtqa2uIvMpIAAQJ5BDyn8sBoJkCAAAECBAgQIECAAAECBAgQIECAAAECBAhUqYCgXJUW1m0RIECAAIFKEEhebzhgm17RsXPyT46FO2rm18a7j07wutWF43MVAQLNCHhONQOkmwABAgQIECBAgAABAgQIECBAgAABAgQIECBQJQKCclVSSLdBgAABAgQqVWDxZbrGgK17xUprLx0dOxUfmKtZUBv/euvLePexCTFr6txKvT3rIkCgCgQ8p6qgiG6BAAECBAgQIECAAAECBAgQIECAAAECBAgQINCMgKBcM0C6CRAgQIAAgfIIdO3RuT4st/TK3WOJ5RaLbkt2iS6LdYoOdeG52rpQ3LzZC2LOtHkxfdLs+PLTmfUhubkz5pfny81CgACBIgQ8p4pAMoQAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgkFIBQbmUFs6yCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKA4AUG54pyMIkCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGUCgjKpbRwlk2AAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECxQkIyhXnZBQBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIpFRAUC6lhbNsAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEChOQFCuOCejCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQCClAoJyKS2cZRMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAcQKCcsU5GUWAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECKRUQlEtp4SybAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBIoTEJQrzskoAgQIECBAoEoFui/bNWZOmVuld9e+bkstq6fealk9tXQnBAgQIECAAAECBAgQIECAAAECBAgQIECAAIFKERCUq5RKWAcBAgQIECDQ6gJdFusUWx25djxxxVsxb/aCVv9+X1g+AbUsn2Vbz6SWbV0B30+AAAECBAgQIECAAAECBAgQIECAAAECBAgQqE4BQbnqrKu7IkCAAAECBIoQWHPb3jFgm17x7qMT4p1HPiviCkMqVUAtK7Uypa9LLUs3cwUBAgQIECBAgAABAgQIECBAgAABAgQIECBAgEDzAoJyzRsZQYAAAQIECFShQLJr1bbHrhOd637Or9tN7pGL37CrXErrrJYpLVyOZatlDhRNBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECZREQlCsLo0kIECBAgACBtAk07FrVsG67yjVIpO+nWqavZvlWrJb5ZLQTIECAAAECBAgQIECAAAECBAgQIECAAAECBAgsqoCg3KIKup4AAQIECBBInUDjXasaFm9XuQaJdP1Uy3TVq9Bq1bKQjj4CBAgQIECAAAECBAgQIECAAAECBAgQIECAAIFFFRCUW1RB1xMgQIAAAQKpE8jctarhBuwq1yCRnp9qmZ5aNbdStWxOSD8BAgQIECBAgAABAgQIECBAgAABAgQIECBAgMCiCAjKLYqeawkQIECAAIHUCeTatarhJuwq1yCRjp9qmY46FbNKtSxGyRgCBAgQIECAAAECBAgQIECAAAECBAgQIECAAIFFERCUWxQ91xIgQIAAAQKpE8i3a1XDjdhVrkGi8n+qZeXXqNgVqmWxUsYRIECAAAECBAgQIECAAAECBAgQIECAAAECBAgsrICg3MLKuY4AAQIECBBInUChXasabsaucg0Slf1TLSu7PqWsTi1L0TKWAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBgYQUE5RZWznUECBAgQIBA6gSa27Wq4YbsKtcgUbk/1bJya1PqytSyVDHjCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEFkZAUG5h1FxDgAABAgQIpE6gmF2rGm7KrnINEpX5Uy0rsy4Lsyq1XBg11xAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECCyMgKDcwqi5hgABAgQIEEidQLG7VjXcmF3lGiQq76daVl5NFnZFarmwcq4jQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAoVUBQrlQx4wkQIECAAIHUCZSya1XDzdlVrkGisn6qZWXVY1FWo5aLoudaAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBUgUE5UoVM54AAQIECBBInUCpu1Y13KBd5RokKuenWlZOLRZ1JWq5qIKuJ0CAAAECBAgQIECAAAECBAgQIECAAAECBAgQKEVAUK4ULWMJECBAgACBVAqsuV3v6NytU9baVx+6wtdt45794uvPDR/mz1kQ7/zzs4ZTPytAQC0roAhlWoJalgnSNAQIECBAgAABAgQIECBAgAABAgQIECBAgAABAkUJCMoVxWQQAQIECBAgUI0Cu5654de39Y+zXv76sw/pE1DL9NUs34rVMp+MdgIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgUUREJRbFD3XEiBAgAABAqkWEMhJdfmaLF4tm3Ck+kQtU10+iydAgAABAgQIECBAgAABAgQIECBAgAABAgQIVKyAoFzFlsbCCBAgQIAAgZYWEMhpaeHWm18tW8+6pb9JLVta2PwECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgfYpICjXPuvurgkQIECAAIE6AYGc6vk1UEu1rB4Bd0KAAAECBAgQIECAAAECBAgQIECAAAECBAgQINASAoJyLaFqTgIECBAgQCAVAsJVqShTUYtUy6KYUjFILVNRJoskQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECKROQFAudSWzYAIECBAgQKBcAgI55ZJs+3nUsu1rUK4VqGW5JM1DgAABAgQIECBAgAABAgQIECBAgAABAgQIECDQWEBQrrGGzwQIECBAgEC7EhDIqZ5yq6VaVo+AOyFAgAABAgQIECBAgAABAgQIECBAgAABAgQIEGgJAUG5llA1JwECBAgQIJAKAeGqVJSpqEWqZVFMqRiklqkok0USIECAAAECBAgQIECAAAECBAgQIECAAAECBFInICiXupJZMAECBAgQIFAuAYGcckm2/Txq2fY1KNcK1LJckuYhQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBoLCAo11jDZwIECBAgQKBdCQjkVE+51VItq0fAnRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECLSEgKBcS6iakwABAgQIEEiFgHBVKspU1CLVsiimVAxSy1SUySIJECBAgAABAgQIECBAgAABAgQIECBAgAABAqkTEJRLXcksmAABAgQIECiXgEBOuSTbfh61bPsalGsFalkuSfMQIECAAAECBAgQIECAAAECBAgQIECAAAECBAg0FhCUa6zhMwECBAgQINCuBARyqqfcaqmW1SPgTggQIECAAAECBAgQIECAAAECBAgQIECAAAECBFpCQFCuJVTNSYAAAQIECKRCQLgqFWUqapFqWRRTKgapZSrKZJEECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgdQJCMqlrmQWTIAAAQIECJRLQCCnXJJtP49atn0NyrUCtSyXpHkIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQaCwjKNdbwmQABAgQIEGhXAgI51VNutVTL6hFwJwQIECBAgAABAgQIECBAgAABAgQIECBAgAABAi0hICjXEqrmJECAAAECBFIhIFyVijIVtUi1LIopFYPUMhVlskgCBAgQIECAAAECBAgQIECAAAECBAgQIECAQOoEBOVSVzILJkCAAAECBMolIJBTLsm2n0ct274G5VqBWpZL0jwECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQKNBQTlGmv4TIAAAQIECLQrAYGc6im3Wqpl9Qi4EwIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgZYQEJRrCVVzEiBAgAABAqkQEK5KRZmKWqRaFsWUikFqmYoyWSQBAgQIECBAgAABAgQIECBAgAABAgQIECBAIHUCgnKpK5kFEyBAgAABAuUSEMgpl2Tbz6OWbV+Dcq1ALcslaR4CBAgQIECAAAECBAgQIECAAAECBAgQIECAAIHGAoJyjTV8JkCAAAECBNqVgEBO9ZRbLdWyegTcCQECBAgQIECAAAECBAgQIECAAAECBAgQIECAQEsICMq1hKo5CRAgQIAAgVQICFelokxFLVIti2JKxSC1TEWZLJIAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgkDoBQbnUlcyCCRAgQIAAgXIJCOSUS7Lt51HLtq9BuVagluWSNA8BAgQIECBAgAABAgQIECBAgAABAgQIECBAgEBjAUG5xho+EyBAgAABAu1KQCCnesqtlmpZPQLuhAABAgQIECBAgAABAgQIECBAgAABAgQIECBAoCUEBOVaQtWcBAgQIECAQCoEhKtSUaaiFqmWRTGlYpBapqJMFkmAAAECBAgQIECAAAECBAgQIECAAAECBAgQSJ2AoFzqSmbBBAgQIECAQLkEBHLKJdn286hl29egXCtQy3JJmocAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQKCxgKBcYw2fCRAgQIAAgXYlIJBTPeVWS7WsHgF3QoAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAg0BICgnItoWpOAgQIECBAIBUCwlWpKFNRi1TLophSMUgtU1EmiyRAgAABAgQIECBAgAABAgQIECBAgAABAgQIpE5AUC51JbNgAgQIECBAoFwCAjnlkmz7edSy7WtQrhWoZbkkzUOAAAECBAgQIECAAAECBAgQIECAAAECBAgQINBYQFCusYbPBAgQIECAQLsSEMipnnKrpVpWj4A7IUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQaAkBQbmWUDUnAQIECBAgkAoB4apUlKmoRaplUUypGKSWqSiTRRIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEUicgKJe6klkwAQIECBAgUC4BgZxySbb9PGrZ9jUo1wrUslyS5iFAgAABAgQIECBAgAABAgQIECBAgAABAgQIEGgsICjXWMNnAgQIECBAoF0JCORUT7nVUi2rR8CdECBAgAABAgQIECBAgAABAgQIECBAgAABAgQItISAoFxLqJqTAAECBAgQSIWAcFUqylTUItWyKKZUDFLLVJTJIgkQIECAAAECBAgQIECAAAECBAgQIECAAAECqRMQlEtdySyYAAECBAgQKJeAQE65JNt+HrVs+xqUawVqWS5J8xAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECDQWEJRrrOEzAQIECBAg0K4EBHKqp9xqqZbVI+BOCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEWkJAUK4lVM1JgAABAgQIpEJAuCoVZSpqkWpZFFMqBqllKspkkQQIECBAgAABAgQIECBAgAABAgQIECBAgACB1AkIyqWuZBZMgAABAgQIlEtAIKdckm0/j1q2fQ3KtQK1LJekeQgQIECAAAECBAgQIECAAAECBAgQIECAAAECBBoLCMo11vCZAAECBAgQaFcCAjnVU261VMvqEXAnBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECLSEgKNcSquYkQIAAAQIEUiEgXJWKMhW1SLUsiikVg9QyFWWySAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBA6gQE5VJXMgsmQIAAAQIEyiUgkFMuybafRy3bvgblWoFalkvSPAQIECBAgAABAgQIECBAgAABAgQIECBAgAABAo0FBOUaa/hMgAABAgQItCsBgZzqKbdaqmX1CLgTAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBlhAQlGsJVXMSIECAAAECqRAQrkpFmYpapFoWxZSKQWqZijJZJAECBAgQIECAAAECBAgQIECAAAECBAgQIEAgdQKCcqkrmQUTIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQCkCgnKlaBlLgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAqkTEJRLXcksmAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRKERCUK0XLWAIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBInYCgXOpKZsEECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUIqAoFwpWsYSIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQOoEBOVSVzILJkCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAEC/7+9O42x8yrvAP7MPvbYHjvjJYzHjnd7cOIsxYmzqgkOWxeE2lK1NEAptAUqFanQ0s8gpEIpHyoWUaq0tJSWDywtYo0DwmCFbMRL44XYiWN76HjNLJ7F1zPTewd8M2fsjOfaY8+9r3+vFPl9zj3ve8/5HV/Jkf46hwABAgQIECBQioCgXCla+hIgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAxQkIylXckhkwAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECJQiIChXipa+BAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFBxAoJyFbdkBkyAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECpQgIypWipS8BAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIVJyAoFzFLZkBEyBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEApAoJypWjpS4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIVJyAoV3FLZsAECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUIqAoFwpWvoSIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQMUJCMpV3JIZMAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAiUIiAoV4qWvgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBQcQKCchW3ZAZMgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAqUICMqVoqUvAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECFScgKBcxS2ZARMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAKQKCcqVo6UuAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECFScgKFdxS2bABAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIFCKgKBcKVr6EiBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEDFCQjKVdySGTABAgQIECBwOQKNzfVx/brmmDG3IRrn1EVVTVUMdp+J/q5cHPt5V/QcHbic13t2mgTmts2M2vqa4rf3dPbH4OmzxdpN+QnUNdZEy/LZMSP/m2yYXZv/ry7qGmpioCcXvccH8/8NRO/R/hjozpXf4I2IAAECBAgQIECAAAECBAgQIECAAAECBAgQIECg4gQE5SpuyQyYAAECBAgQuBSBmfMa4tVvXBwLVs6JqC78E+jC10uHT8fu7xyOl470XbiD1rISKASt1tz/qpi7pCkZ19P/dSA693QlbYrpF6iurYolt82PRfmw6nU3zIqqCX6Lo6MdiTj8sxOxd0tHnOkTfJz+FTQCAgQIECBAgAABAgQIECBAgAABAgQIECBAgEDlCgjKVe7aGTkBAgQIECAwSYGFa+bEzW9ZFrX5Hawmc40MDcfu7x6Jg08cn0x3faZBYF4+GFcIyF2XD8pd6BKUu5DK9Lc1NNXGAx+8qeSB5PqHYu8jR+LQ0ydKftYDBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAoCgnL+HhAgQIAAAQKZFigcybnpnavzR6xWlzzP7V99ITp2nir5OQ9cOYHm1pmjAbn5q/I7A05wCcpNgDPNH23+6w1RN2NyodXxQ33qy/vj6L7u8c1qAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAhcVEJS7KJEOBAgQIECAQKUK1OV3kLvnz9dFY3N9MoXh3HAc2XkyTr14OkaGRmLWohmx5Nbror6p7rx+276wN3qODiTtiukRWLZpYbS/fvGkvlxQblJM09Jp07vWRGFHwMLVe2wgTjzfM/pn38nBqJ9ZG00tDbFg9ZxoXpwep1vof6Y3F1s/u8cxrAUMFwECBAgQIECAAAECBAgQIECAAAECBAgQIECAQEkCgnIlcelMgAABAgQIVJLAmte2xsp7FiVDPn1iIJ780oHoOzWYtNfUVcfNv7MsFq1tTtqPPded778/aVNMj8C61y2O5XcuPP/LR/JNhX/VjrkE5cZglNlt260tUZ/fUa5zb1ecPpH+DotDza9n2y0t0Z5f8/FHJhd2eSzs9ugiQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgUIqAoFwpWvoSIECAAAECFSNQ21AT939gfRKyOTswlN+NancMdOcuOI+qqqq4812ro7kt3clq2z/tja6Ovgs+o/HqCYwPyuX6h+KFx47G4e0nR9d67EgE5cZqVO5964Z5cfNbliUTyPWdjUc+sTNpUxAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBC4mICg3MWEfE6AAAECBAhUpMCFjul89tuH4+Djxyacz+yFjXHPe9uTPh07Tsb2rx1M2hRXX+BcUO5cQO6Fnx6Ns4PDUQhFPvjhDcmABOUSjoouNj60MuavmJPM4dF/2BWDPRcOvCYdFQQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgR+JSAo568CAQIECBAgkEmB29++KlqWzy7ObejMUDz6yV1x9sxwse2Vbu54x+q4btms4se5/rOx5RO7YmSkcMana7oE5rbNHA1MnQvInRuHoNw5iWz+ueLuRbF2c2syuSfyxyEfzx+L7CJAgAABAgQIECBAgAABAgQIECBAgAABAgQIECAwWQFBuclK6UeAAAECBAhUjEBtQ3Vs/tBNUVVTXRxzx878rnBfndyucEtua4kbf2tp8dnCzWMP74tTL55O2hTlISAoVx7rcKVGsWhdc9z2+yuS1z/7rUNx8InjSZuCAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAwEQCgnIT6fiMAAECBAgQqEiB+avmxMa3rUzGvuubh+LQU5ML1lzo+NV9Wzpi/487k3cqykNAUK481uFKjWJR+9y47a3Lk9dv/+oL0bHzVNKmIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIDCRgKDcRDo+I0CAAAECBCpSYNmmhdH++sXJ2H/8uT3R09mftL1SUVVVFQ9++Kaoqa8pdjmy/WTs+PrkdqQrPuTmqggIyl0V5mn7kmV3LIj2N7Ql32+Hx4RDQYAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgMAkBQblJIOlCgAABAgQIVJbAjb+5JJb82vxk0N/72PYYyg0nbRMV9753XcxaOKPYpevI6dj2hX3F2k35CAjKlc9aXImRXOj3/INP7YqB7tyV+DrvJECAAAECBAgQIECAAAECBAgQIECAAAECBAgQyKiAoFxGF9a0CBAgQIDAtSxw+9tXRcvy2UWCXP9QPPLxHcW6cFPXWJMPwjWOtvUeHYjcwFDyeeHo1sIRrueuXN/ZeOQTO8+V/iwjAUG5MlqMKR5KYXfHB/5qfdQ31RXf3HtsILZ+ZnexdkOAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEBgMgKCcpNR0ocAAQIECBCoKIG73rMmmlubimPuPdofWz+7p1jfcPuCWLe5NarrqkfbhvM7ze3d0hEv/PRYsc+GNy+Nxbe0FOvhoeH47ke3F2s35SMgKFc+azHVI5m/YnZsfGhV8tp9j3bE/q2dSZuCAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAwMUEBOUuJuRzAgQIECBAoOIE7n1fe8xa8Mvd4gqDH3tsasuyWXH721dHFP4VNO564t+fi+P7e0Zb17+xLZbmA3Vjr+985JkYGR4Z2+S+DAQE5cpgEa7QEMbv7Bj5398P//HZ6H/pzBX6Rq8lQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBDIqoCgXFZX1rwIECBAgMA1LHD/B9ZHY3N9UeDkwd746b/8fLRe/6YlsXTj/OJnY2+OPHMydnzj4GjTute1xvI7F439OB75ux3nHdGadFBMi4Cg3LSwX/EvXdQ+N2576/Lke57f1hl7vt+RtCkIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQITEZAUG4ySvoQIECAAAECFSXw2g/eGPVNdcUxn3i+Jx7/4nOj9Wv+YEUsWNNc/GzszckX8oG6f/1loG7ta1tjxT1pUO7RT+6Kwd7c2Efcl4GAoFwZLMIUD6Emfyzyfe9vTwKv/afO5I9Q3h1D+aOSXQQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRKFRCUK1VMfwIECBAgQKDsBSbaUe7V+SNVbxh3pOq5CR155kR+R7kXR0s7yp1TKf8/BeXKf41KHeG6zfkdHe9Og6pjj0Yu9X36EyBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEBCU83eAAAECBAgQyJzAve9rj1kLGovz6jpyOrZ9Yd9oPW9pU2x655qIwr+Cxl1jgzjr84G6peMCdd/5yDMxMjwy7inldAsIyk33Ckzt989e2Bh3/+naqKqpLr64Y8fJ2P61g8XaDQECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIFSBQTlShXTnwABAgQIECh7gbvevSaaFzcVx9l7tD9/ZOOeYr3iroVROFo1qn+VlsuH3/b98Bexf2tnsc+GNy+Nxbe0FOvhoeH47ke3F2s35SMgKFc+a3G5I6nK/ybveMeqmLd0VvFVub6z8aNP744z+T9dBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBC5VQFDuUuU8R4AAAQIECJStwMaHVsb8FXOK48v1D8UjH99RrAs3M+c1xNzFM0d3lnvpcF/0nRpMPt/4R/l3rHz5HWdO52LL3+9K+ijKQ0BQrjzWYSpGcd7RyPkNHJ/+yoHo3NM1Fa/3DgIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgWtYQFDuGl58UydAgAABAlkVWP8bS2Lpa+Yn0/vex7bHUG44aZuoGH9866kXe+Oxh38+0SM+myYBQblpgp/iry38Zgu/3bHXvkc7kp0ex37mngABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEApAoJypWjpS4AAAQIECFSEwLI7FkT7G9qSsf74c3uip7M/aXuloqqqKh782w1RU1dd7HL4Zydi53+/WKzdlI+AoFz5rMWljqSwe+PGP1zx8nHI+RcdeeZE7PiG39ylmnqOAAECBAgQIECAAAECBAgQIECAAAECBAgQIEAgFRCUSz1UBAgQIECAQAYERkM3+aNTx167/ufFOPT0ibFNr3g/5/oZcfefrUs+3/v9I3Fg29GkTVEeAoJy5bEOlzqK2YtmxKZ3ro7axpriK44f6I4nv3QgRobzZ6+6CBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECEyBgKDcFCB6BQECBAgQIFBeArUN1bH5QzdFVc3LO8J1bD8Z279+cFIDvWHj/Hj1m9IjIH/y+b3R/Yu+ST2v09UVEJS7ut5T+W2Nc+rizj9ZE41z6ouvLez8+NjD++Ls4OSPSi4+7IYAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIDAKwgIyr0CjGYCBAgQIECgsgU2PrQy5q+YU5zE0OBQbPnkrhjKXTx8UwjuzG1rKj472JOLRz+1K8LmVkWTcroRlCun1Zj8WAqB1k1/vCYKO8qduwa6zsS2f94Xhd+ciwABAgQIECBAgAABAgQIECBAgAABAgQIECBAgMBUCgjKTaWmdxEgQIAAAQJlI3ChXeGe/dahOPjE8QnH2Nw6M+56z9qkz6Gnjseubx5K2hTlIyAoVz5rMdmRVNdUxWvetjJals8uPpLrHxrdSa732ECxzQ0BAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBqRIQlJsqSe8hQIAAAQIEykqgsFvVr//l+qibUVsc19mBodj62d0x0H3h3aoK4Z0737025lz/8g5XhV3ktn5md/QeF94pQpbZjaBcmS3IxYaT/z+Qm9+yLFpvmlfsOZzf6fHxf3suTh06XWxzQ4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQGAqBQTlplLTuwgQIECAAIGyElh576JY80BrMqa+k4Px5H/sj9MnBpP2usaauOX3liXHtRY6dO5+KZ7+yvNJX0V5CQjKldd6XGw06x5sjeV3LXq52/DI6G+sc2/Xy23uCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECEyxgKDcFIN6HQECBAgQIFA+AjV11aPHqM5a0JgMamRoOH7x7EvRdaQvqmuromF2fbTdfF3U5sNyY6/CDnQ/+fze6DuVhurG9nF/9QTue3971NRXn/eFVVWFNaxL2nP9Z2Mov0vZ+KtzT1c8++3D45vVV0mg7daWuOm3l573bQPdZ85rm6ih5//648kvH5ioi88IECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIJAKCcgmHggABAgQIEMiaQFNLQ9yVP051fAhuMvMs7CRX2FHOVR4CD/7Nhktax7GjP/F8Tzz+xefGNrm/igLr39gWS29fcNnfWAjW/eBT/3vZ7/ECAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBa0dAUO7aWWszJUCAAAEC16zA3LaZcevvLo/G5vpJGQzndyLb+c1D0bHj5KT663R1BATlro7zlfwWQbkrqevdBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECEwkIyk2k4zMCBAgQIEAgMwJ1M2pj7eZXReuN113w+M7RiQ6PxLHnumPvlo7oOTqQmblnZSL3/UV7NLWkx+iWOreje7viqf90ZGepblPVf9V918fq+1912a87fXwgfvTp3Zf9Hi8gQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBC4dgQE5a6dtTZTAgQIECBAIC9Q21AdC1Y1x8x59VHfVBf9XWeitr46BrpzcXx/dwz05DgRIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQMYEBOUytqCmQ4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQKpgKBc6qEiQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgYwJCMplbEFNhwABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRSAUG51ENFgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAhkTEJTL2IKaDgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAikAoJyqYeKAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBDImICiXsQU1HQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBIBQTlUg8VAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECGRMQFAuYwtqOgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCQCgjKpR4qAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEMiYgKBcxhbUdAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEAgFRCUSz1UBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIJAxAUG5jC2o6RAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAKiAol3qoCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQCBjAoJyGVtQ0yFAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBVEBQLvVQESBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEDGBATlMragpkOAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECqYCgXOqhIkCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGMCQjKZWxBTYcAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEUgFBudRDRYAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIZExCUy9iCmg4BAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIpAKCcqmHigABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQyJiAol7EFNR0CBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQSAUE5VIPFQECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAhkTEBQLmMLajoECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgkAoIyqUeKgIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBDImICgXMYW1HQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAIBUQlEs9VAQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCQMQFBuYwtqOkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQCogKJd6qAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEAgYwKCchlbUNMhQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgVRAUC71UBEgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAxgQE5TK2oKZDgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAqmAoFzqoSJAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBjAkIymVsQU2HAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBFIBQbnUQ0WAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECGRMQlMvYgpoOAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECKQCgnKph4oAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEMiYgKJexBTUdAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEEgFBOVSDxUBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIZExAUC5jC2o6BAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIJAKCMqlHioCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQyJiAoFzGFtR0CBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQCAVEJRLPVQECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgkDEBQbmMLajpECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEAqICiXeqgIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAIGMCgnIZW1DTIUCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIFUQFAu9VARIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQMYEBOUytqCmQ4AAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQFZS7bQAAAP8SURBVIAAAQKpgKBc6qEiQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgYwJCMplbEFNhwABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgRSAUG51ENFgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAhkTEJTL2IKaDgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAikAoJyqYeKAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBDImICiXsQU1HQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBIBQTlUg8VAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECGRMQFAuYwtqOgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCQCgjKpR4qAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEMiYgKBcxhbUdAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEAgFRCUSz1UBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIJAxAUG5jC2o6RAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAKiAol3qoCBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQCBjAoJyGVtQ0yFAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBVEBQLvVQESBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgEDGBATlMragpkOAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECqYCgXOqhIkCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIGMCQjKZWxBTYcAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEUgFBudRDRYAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIZExCUy9iCmg4BAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIpAKCcqmHigABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQyJvD/YRAl0XZrglcAAAAASUVORK5CYII=)"
      ],
      "metadata": {
        "id": "VuqmgvG3v6kS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **NOTE**\n",
        "\n",
        "We use lowercase letters for scalar and vector and uppercase letters for MATRIX and TENSOR. This is on purpose. In practice, you'll often see scalars and vectors denoted as lowercase letters such as y or a. And matrices and tensors denoted as uppercase letters such as X or W.\n",
        "\n",
        "You also might notice the names martrix and tensor used interchangably. This is common. Since in PyTorch you're often dealing with torch.Tensors (hence the tensor name), however, the shape and dimensions of what's inside will dictate what it actually is."
      ],
      "metadata": {
        "id": "FQUnK2uJwGmP"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Un807w8yCq25"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ***Random Tensors***\n",
        "\n",
        "We've learnt tensors represent some form of data.\n",
        "\n",
        "And machine learning models such as neural networks manipulate and seek patterns within tensors.\n",
        "\n",
        "But when building ML models with PyTorch, it's rare you'll create tensors by hand (like what we've being doing).\n",
        "\n",
        "Instead, a machine learning model often starts out with large random tensors of numbers and adjusts these random numbers as it works through data to better represent it.\n",
        "\n",
        "In essence:\n",
        "\n",
        "Start with random numbers -> look at data -> update random numbers -> look at data -> update random numbers...\n",
        "\n",
        "As a data scientist, you can define how the machine learning model starts (initialization), looks at data (representation) and updates (optimization) its random numbers."
      ],
      "metadata": {
        "id": "GCQwlS11wfZB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating Random Tensor of size (3,4)\n",
        "\n",
        "random_tensor = torch.rand(size=(3,4))\n",
        "random_tensor\n"
      ],
      "metadata": {
        "id": "yc_xRl_3wcKD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2481d1d5-a308-4b30-c95f-79fdf37a50ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0.2348, 0.8625, 0.4230, 0.1228],\n",
              "        [0.2725, 0.0582, 0.5363, 0.9808],\n",
              "        [0.4184, 0.8743, 0.8947, 0.3430]])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "The flexibility of torch.rand() is that we can adjust the size to be whatever we want.\n",
        "\n",
        "For example, say you wanted a random tensor in the common image shape of [224, 224, 3] ([height, width, color_channels])."
      ],
      "metadata": {
        "id": "9Kl3NOfZbh77"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating a Random Tensor of size (224, 224, 3)\n",
        "\n",
        "random_image_size_tensor = torch.rand(size=(224, 224, 3))\n",
        "random_image_size_tensor"
      ],
      "metadata": {
        "id": "P9NxfMftwcoP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7c1222cc-18ec-4717-848c-40b1d6430a99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[[0.1986, 0.8562, 0.3365],\n",
              "         [0.5355, 0.2445, 0.8402],\n",
              "         [0.4235, 0.6501, 0.5169],\n",
              "         ...,\n",
              "         [0.5407, 0.9914, 0.4590],\n",
              "         [0.7139, 0.3669, 0.5856],\n",
              "         [0.6026, 0.4432, 0.5399]],\n",
              "\n",
              "        [[0.4684, 0.5388, 0.2556],\n",
              "         [0.5900, 0.2352, 0.9856],\n",
              "         [0.5868, 0.6907, 0.8990],\n",
              "         ...,\n",
              "         [0.3516, 0.1181, 0.3016],\n",
              "         [0.1062, 0.2560, 0.7612],\n",
              "         [0.1339, 0.4288, 0.2749]],\n",
              "\n",
              "        [[0.8749, 0.8889, 0.9480],\n",
              "         [0.5251, 0.4263, 0.0030],\n",
              "         [0.3874, 0.5052, 0.8438],\n",
              "         ...,\n",
              "         [0.8202, 0.8039, 0.3875],\n",
              "         [0.2503, 0.8735, 0.7780],\n",
              "         [0.3348, 0.2290, 0.2448]],\n",
              "\n",
              "        ...,\n",
              "\n",
              "        [[0.3501, 0.0671, 0.8444],\n",
              "         [0.2371, 0.2661, 0.8777],\n",
              "         [0.6414, 0.6115, 0.7377],\n",
              "         ...,\n",
              "         [0.1213, 0.0056, 0.9398],\n",
              "         [0.0842, 0.8934, 0.6029],\n",
              "         [0.3735, 0.1062, 0.7002]],\n",
              "\n",
              "        [[0.7379, 0.6160, 0.3542],\n",
              "         [0.0818, 0.9149, 0.2878],\n",
              "         [0.1408, 0.1582, 0.5832],\n",
              "         ...,\n",
              "         [0.4853, 0.1197, 0.3249],\n",
              "         [0.2931, 0.8025, 0.4962],\n",
              "         [0.2287, 0.6608, 0.3771]],\n",
              "\n",
              "        [[0.2024, 0.2474, 0.3300],\n",
              "         [0.7113, 0.7295, 0.5091],\n",
              "         [0.2854, 0.8483, 0.6032],\n",
              "         ...,\n",
              "         [0.4298, 0.0434, 0.5493],\n",
              "         [0.7042, 0.8122, 0.8573],\n",
              "         [0.3829, 0.5891, 0.8367]]])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "random_image_size_tensor.shape\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AwtlT9PIb8YR",
        "outputId": "35632c1c-4ae9-4f46-b904-8cc5b88c7d89"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.Size([224, 224, 3])"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "random_image_size_tensor.ndim"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OMIp7KGqcVNg",
        "outputId": "3ce5057b-8cec-451a-ff1e-139fa41823a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "random_image_size_tensor2 = torch.rand(size=(255, 255, 4))\n",
        "\n",
        "random_image_size_tensor2, random_image_size_tensor2.shape, random_image_size_tensor2.ndim"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OKUiKx5WcWWQ",
        "outputId": "8e25162a-3496-41fe-d054-b996d8bba459"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor([[[0.0868, 0.3151, 0.4953, 0.4696],\n",
              "          [0.7296, 0.4024, 0.5135, 0.9850],\n",
              "          [0.6003, 0.0726, 0.2595, 0.4464],\n",
              "          ...,\n",
              "          [0.1314, 0.2735, 0.9542, 0.8087],\n",
              "          [0.5189, 0.7848, 0.5867, 0.5530],\n",
              "          [0.5020, 0.3467, 0.0818, 0.1513]],\n",
              " \n",
              "         [[0.0297, 0.4486, 0.0082, 0.3361],\n",
              "          [0.9020, 0.7625, 0.6297, 0.9598],\n",
              "          [0.3790, 0.3133, 0.1299, 0.7073],\n",
              "          ...,\n",
              "          [0.4851, 0.9891, 0.3283, 0.6993],\n",
              "          [0.1069, 0.7742, 0.3017, 0.2124],\n",
              "          [0.9574, 0.7371, 0.2287, 0.3519]],\n",
              " \n",
              "         [[0.7504, 0.6991, 0.4780, 0.5038],\n",
              "          [0.7993, 0.3088, 0.1576, 0.9510],\n",
              "          [0.5154, 0.1298, 0.9488, 0.5877],\n",
              "          ...,\n",
              "          [0.1958, 0.4904, 0.0838, 0.7890],\n",
              "          [0.6650, 0.7829, 0.0847, 0.1818],\n",
              "          [0.8867, 0.7586, 0.0579, 0.9904]],\n",
              " \n",
              "         ...,\n",
              " \n",
              "         [[0.4837, 0.5876, 0.8246, 0.9798],\n",
              "          [0.1092, 0.1323, 0.2686, 0.8505],\n",
              "          [0.7690, 0.9839, 0.9598, 0.3664],\n",
              "          ...,\n",
              "          [0.0216, 0.6164, 0.5821, 0.4814],\n",
              "          [0.4527, 0.1409, 0.4239, 0.3310],\n",
              "          [0.2679, 0.1758, 0.2484, 0.5751]],\n",
              " \n",
              "         [[0.2290, 0.5194, 0.8703, 0.8844],\n",
              "          [0.0194, 0.1095, 0.8457, 0.9298],\n",
              "          [0.9021, 0.1354, 0.3127, 0.2361],\n",
              "          ...,\n",
              "          [0.2798, 0.7699, 0.6212, 0.2483],\n",
              "          [0.6156, 0.6654, 0.5091, 0.5009],\n",
              "          [0.1199, 0.5635, 0.1203, 0.2732]],\n",
              " \n",
              "         [[0.5329, 0.8077, 0.1896, 0.3066],\n",
              "          [0.6909, 0.6306, 0.7222, 0.2865],\n",
              "          [0.4259, 0.7519, 0.5408, 0.7824],\n",
              "          ...,\n",
              "          [0.1627, 0.1677, 0.0128, 0.6188],\n",
              "          [0.2664, 0.9393, 0.3280, 0.9543],\n",
              "          [0.5897, 0.4729, 0.0655, 0.7293]]]),\n",
              " torch.Size([255, 255, 4]),\n",
              " 3)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dMBurPx3ctAh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ***Zeros and Ones***\n",
        "\n",
        "Sometimes you'll just want to fill tensors with zeros or ones.\n",
        "\n",
        "This happens a lot with masking (like masking some of the values in one tensor with zeros to let a model know not to learn them).\n",
        "\n",
        "Let's create a tensor full of zeros with torch.zeros()\n",
        "\n",
        "We use size() parameter again"
      ],
      "metadata": {
        "id": "SSVYqhCuddcO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating a Tensor of all zeros\n",
        "\n",
        "zeros = torch.zeros(size=(3,4))\n",
        "zeros"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dgr16sKidioi",
        "outputId": "58f36249-4b45-464f-c589-98364685ea3b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[0., 0., 0., 0.],\n",
              "        [0., 0., 0., 0.],\n",
              "        [0., 0., 0., 0.]])"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ones = torch.ones(size=(3,4))\n",
        "ones"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O2AGhbsidieJ",
        "outputId": "0b40b51d-e708-4cca-9070-349bb64a1454"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[1., 1., 1., 1.],\n",
              "        [1., 1., 1., 1.],\n",
              "        [1., 1., 1., 1.]])"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7gTsOjr1d_aR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ***Creating a Range***\n",
        "\n",
        "Sometimes you might want a range of numbers, such as 1 to 10 or 0 to 100.\n",
        "\n",
        "You can use torch.arange(start, end, step) to do so.\n",
        "\n",
        "\n",
        "#### *Note*:\n",
        "In Python, you can use range() to create a range. However in PyTorch, torch.range() is deprecated and may show an error in the future.\n",
        "\n"
      ],
      "metadata": {
        "id": "1ZSRUzIWeBsw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating a range of values using arange()\n",
        "zero2ten = torch.arange(0, 10, 1)  # its not arrange its \"a range\", arange\n",
        "zero2ten"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IJ9g7eZKeBQW",
        "outputId": "7318a812-5de2-4729-dde8-273bf4ba9163"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating a range of values using range()  [Not recommended]\n",
        "\n",
        "zeroTen = torch.range(0,10) # avoid using .range() as it may return an error\n",
        "zeroTen\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PDJcMzGIeBZw",
        "outputId": "c7b91fb4-3f9a-4cc8-8b8d-30f20c674d1d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-27-f5da3200863e>:3: UserWarning: torch.range is deprecated and will be removed in a future release because its behavior is inconsistent with Python's range builtin. Instead, use torch.arange, which produces values in [start, end).\n",
            "  zeroTen = torch.range(0,10) # avoid using .range() as it may return an error\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.])"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Sometimes you might want one tensor of a certain type with the same shape as another tensor.\n",
        "\n",
        "For example, a tensor of all zeros with the same shape as a previous tensor.\n",
        "\n",
        "To do so you can use torch.zeros_like(input) or torch.ones_like(input) which return a tensor filled with zeros or ones in the same shape as the input respectively."
      ],
      "metadata": {
        "id": "nzg-RAKvkXBu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Can also create a tensor of zeros similar to another tensor\n",
        "tenZeros = torch.zeros_like(input=zero2ten)\n",
        "tenZeros"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pkROz9L_fPn4",
        "outputId": "ff9066ea-e690-48bd-e21a-dbeed6759192"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "dMS49sfQkV4p"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ***Tensor datatypes***\n",
        "There are many different tensor datatypes available in PyTorch.\n",
        "\n",
        "Some are specific for CPU and some are better for GPU.\n",
        "\n",
        "Generally if you see torch.cuda anywhere, the tensor is being used for GPU.\n",
        "\n",
        "float,\n",
        "\n",
        "    - torch.float32 or torch.float -- default\n",
        "\n",
        "    - torch.float64 or torch.double\n",
        "\n",
        "    - torch.float16 or torch.half\n",
        "\n",
        "\n",
        "there are integers too,\n",
        "\n",
        "float is a decimal like 7.0 whereas integer is no. like 7\n",
        "\n",
        "    - 8-bit, 16-bit, 32-bit, 64-bit integers are there.\n",
        "\n",
        "PRECISION is the reason for all these different datatypes.\n",
        "\n",
        "This matters in deep learning and numerical computing because you're making so many operations, the more detail you have to calculate on, the more compute you have to use.\n",
        "\n",
        "So lower precision datatypes are generally faster to compute on but sacrifice some performance on evaluation metrics like accuracy (faster to compute but less accurate).\n",
        "\n",
        "https://pytorch.org/docs/stable/tensors.html#data-types -- data types\n",
        "\n"
      ],
      "metadata": {
        "id": "lBrPmXEck00P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating a tensor by float32 datatype\n",
        "\n",
        "float_32_tensor = torch.tensor([3.0, 6.0, 9.0])\n",
        "float_32_tensor.dtype"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8GB_fcYfmc82",
        "outputId": "246137a8-6402-420b-d3c3-65c3cd1faf99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.float32"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a = torch.tensor([1,2,3])\n",
        "a.dtype"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n4_0i8O2mdEm",
        "outputId": "8443f0a2-9e05-484a-a8b4-fd24be05093b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.int64"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "float_32_tensor = torch.tensor([3.0, 6.0, 9.0],\n",
        "                               dtype = None,  # defaults to None, which is torch.float32 or whatever datatype is passed\n",
        "                               device = None,  # defaults to None, which uses the default tensor type\n",
        "                               requires_grad = False)  # if True, operations performed on the tensor are recorded\n",
        "\n",
        "float_32_tensor.shape, float_32_tensor.dtype, float_32_tensor.device"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aQKrD7fskVx1",
        "outputId": "0723df7a-ec43-464e-e585-95d7814bb08b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(torch.Size([3]), torch.float32, device(type='cpu'))"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Aside from shape issues (tensor shapes don't match up), two of the other most common issues you'll come across in PyTorch are datatype and device issues.\n",
        "\n",
        "For example, one of tensors is torch.float32 and the other is torch.float16 (PyTorch often likes tensors to be the same format).\n",
        "\n",
        "Or one of your tensors is on the CPU and the other is on the GPU (PyTorch likes calculations between tensors to be on the same device)."
      ],
      "metadata": {
        "id": "CocMQAcBmdTs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "float_16_tensor = torch.tensor([3.0, 6.0, 9.0],\n",
        "                               dtype = torch.float16)  # torch.half would also work\n",
        "\n",
        "float_16_tensor"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tfcjGZhPplxA",
        "outputId": "0a5b7179-ac55-4e5f-bec2-f36b582f1bc3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([3., 6., 9.], dtype=torch.float16)"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "H0g6aPxYrM7a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ***Getting information from tensors***\n",
        "\n",
        "three most common attributes to get information from,\n",
        " - shape - what shape is the tensor? (some operations require\n",
        " specific shape rules)\n",
        " - dtype - what datatype are the elements within the tensor stored in?\n",
        " - device - what device is the tensor stored on? (usually GPU or CPU)\n",
        "\n"
      ],
      "metadata": {
        "id": "_sfl8rkOrMYv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating a Tensor\n",
        "\n",
        "tensurr = torch.rand(size = (3, 4))\n",
        "\n",
        "#tensurr, tensurr.shape, tensurr.dtype, tensurr.device\n",
        "\n",
        "print(f\"tensor: {tensurr}\")\n",
        "print()\n",
        "print(f\"shape of tensor: {tensurr.shape}\")\n",
        "print()\n",
        "print(f\"datatype of tensor: {tensurr.dtype}\")\n",
        "print()\n",
        "print(f\"device being used: {tensurr.device}\")"
      ],
      "metadata": {
        "id": "ogjgVTbAq-uo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "317ce91d-1024-4b1b-9454-44da690175cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor: tensor([[0.4568, 0.6590, 0.3694, 0.0160],\n",
            "        [0.5943, 0.6626, 0.3597, 0.0354],\n",
            "        [0.5960, 0.9370, 0.1616, 0.7979]])\n",
            "\n",
            "shape of tensor: torch.Size([3, 4])\n",
            "\n",
            "datatype of tensor: torch.float32\n",
            "\n",
            "device being used: cpu\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Note:*\n",
        "\n",
        "When you run into issues in PyTorch, it's very often one to do with one of the three attributes.\n",
        "- shape\n",
        "- dtype\n",
        "- device"
      ],
      "metadata": {
        "id": "GAdXtTbIfUYb"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "r5iXjMPcrOaw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ***Manipulating tensors (tensor operations)***\n",
        "\n",
        "- In deep learning, data (images, text, video, audio, protein structures, etc) gets represented as tensors.\n",
        "\n",
        "- A model learns by investigating those tensors and performing a series of operations (could be 1,000,000s+) on tensors to create a representation of the patterns in the input data.\n",
        "\n",
        "- These operations are often a wonderful dance between:\n",
        "    - Addition\n",
        "    - Substraction\n",
        "    - Multiplication (element-wise)\n",
        "    - Division\n",
        "    - Matrix multiplication\n",
        "\n",
        "there are more than the above, but these are the basic building blocks of neural networks\n",
        "\n",
        "Stacking these building blocks in the right way, you can create the most sophisticated of neural networks (just like lego)."
      ],
      "metadata": {
        "id": "g1Q0qO_lftBT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Basic operations\n",
        "\n",
        "\n",
        "# Creating a tensor\n",
        "\n",
        "tensor1 = torch.tensor([1,2,3])\n",
        "tensor1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zJjN4Z44fsyr",
        "outputId": "a38f8c90-da26-4596-bcfa-abf082321c40"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([1, 2, 3])"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Addition\n",
        "\n",
        "tensor1 + 10"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5oSNHr-YjQQz",
        "outputId": "d06dd208-8636-40b0-aa4a-1544d8b3661e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([11, 12, 13])"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Multiplying the tensor by 10\n",
        "\n",
        "tensor1 * 10\n",
        "\n",
        "# tsr  #  don't run \"tsr\" you'd have to run \" tsr * 10 \""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yM1K3lgFfsqu",
        "outputId": "bf311d97-a264-4d84-cbbb-e3bfbe3c5e1f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([10, 20, 30])"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Tensors don't change unless reassigned\n",
        "\n",
        "tensor1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gMSZ4ZBph40n",
        "outputId": "843472f1-a577-48c6-abff-a5ba9f8a4d23"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([1, 2, 3])"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Subtracting and Reassinging\n",
        "\n",
        "tensor1 = tensor1 - 10\n",
        "tensor1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RZ-Gdytvjnve",
        "outputId": "12c491a7-cf32-420a-b361-f307d052d083"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([-9, -8, -7])"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Adding and Reassinging\n",
        "\n",
        "tensor1 = tensor1 + 10\n",
        "tensor1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8CQ8TOZ2jnoU",
        "outputId": "2d48a6f0-47f9-4719-e5e4-b6e528425952"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([1, 2, 3])"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "PyTorch also has a bunch of built-in functions like torch.mul() (short for multiplication) and torch.add() to perform basic operations."
      ],
      "metadata": {
        "id": "f3MOvQFAlmiw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Using torch functions\n",
        "\n",
        "torch.mul(tensor1,10)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gK7ZQxC_lnZF",
        "outputId": "5eaf197d-2cc4-4ae9-baae-e56efa57740c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([10, 20, 30])"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.multiply(tensor1,10)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VGEgBpn2lnxl",
        "outputId": "9840e220-493d-4a5c-b25e-ab6a1e54dd94"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([10, 20, 30])"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "torch.add(tensor1,10)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KIoOLME4i_Zn",
        "outputId": "b55f0573-c6b7-48de-e635-28d10ffb337f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([11, 12, 13])"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Original is still unchanged\n",
        "\n",
        "tensor1\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9yj6MEIXmE1v",
        "outputId": "69587580-6f3a-4292-8800-e63fb082e089"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([1, 2, 3])"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "However, it's more common to use the operator symbols like * instead of torch.mul()"
      ],
      "metadata": {
        "id": "N2TTRmtLmr0n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(tensor1 * tensor1)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CjX8Scl0mrmT",
        "outputId": "3105186e-ebba-4211-b161-d416dea24a35"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([1, 4, 9])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fUyQsfz3mkyO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ***Matrix multiplication***\n",
        "\n",
        "- One of the most common operations in machine learning and deep learning algorithms (like neural networks) is matrix multiplication.\n",
        "\n",
        "- PyTorch implements matrix multiplication functionality in the torch.matmul() method.\n",
        "\n",
        "- The main two rules for matrix multiplication to remember are:\n",
        "    1. The inner dimensions must match:\n",
        "      - (3, 2) @ (3, 2) won't work\n",
        "      - (2, 3) @ (3, 2) will work\n",
        "      - (3, 2) @ (2, 3) will work\n",
        "    2.  The resulting matrix has the shape of the outer dimensions:\n",
        "      - (2, 3) @ (3, 2) -> (2, 2)\n",
        "      - (3, 2) @ (2, 3) -> (3, 3)\n",
        "\n",
        "  *Note: \"@\" in Python is the symbol for matrix multiplication*\n",
        "\n"
      ],
      "metadata": {
        "id": "C3VN2FvqnEPO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "there are two methods,\n",
        "  - element wise multiplication\n",
        "  - matrix mulitplication - the one we use mostly\n",
        "\n",
        "Element-wise multiplication, tensor * tensor\n",
        "\n",
        "```[1*1, 2*2, 3*3] = [1, 4, 9]```\n",
        "\n",
        "\n",
        "Matrix multiplication, torch.matmul(tensor)\n",
        "\n",
        "```[1*1 + 2*2 + 3*3] = [14]```\n",
        "\n",
        "\n",
        "\n",
        "[To learn more about matrix multiplication ](https://www.mathsisfun.com/algebra/matrix-multiplying.html)"
      ],
      "metadata": {
        "id": "IQohkWVxo-jj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tensor1"
      ],
      "metadata": {
        "id": "I2T9AkVlnEEb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e59a446d-151a-4bef-97c1-6e308941077f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([1, 2, 3])"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Element-wise matrix multiplication\n",
        "\n",
        "tensor1 * tensor1"
      ],
      "metadata": {
        "id": "xANQz7U0nD_E",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "786f2ebe-6074-4fba-d625-cbba24672eb5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([1, 4, 9])"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Matrix Multiplication\n",
        "\n",
        "torch.matmul(tensor1, tensor1)"
      ],
      "metadata": {
        "id": "q3B8GA2gnD7C",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4364670a-5e5a-45d2-ef01-ce56858e35c4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(14)"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Matrix Multiplication\n",
        "# Can also use the \"@\" symbol for matrix multiplication, though not recommended\n",
        "\n",
        "tensor1 @ tensor1"
      ],
      "metadata": {
        "id": "iRDh02TJnD2e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a6925b83-9911-46de-ee43-ded1775ade8f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(14)"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-RCopeIi8BhX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ***One of the most common errors in deep learning (shape errors)***\n",
        "\n",
        "Because much of deep learning is multiplying and performing operations on matrices and matrices have a strict rule about what shapes and sizes can be combined, one of the most common errors you'll run into in deep learning is shape mismatches.\n",
        "\n"
      ],
      "metadata": {
        "id": "W6fgN-C38Luo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Shapes need to be in the right way\n",
        "\n",
        "tensorA = torch.tensor([[1, 2],\n",
        "                        [3, 4],\n",
        "                        [5, 6]], dtype = torch.float32)\n",
        "\n",
        "tensorB = torch.tensor([[7, 8],\n",
        "                        [9, 10],\n",
        "                        [11, 12]], dtype = torch.float32)\n",
        "\n",
        "#torch.matmul(tensorA,tensorB)  # this will rise an error"
      ],
      "metadata": {
        "id": "CWSztjiY8INW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "- We can make matrix multiplication work between tensor_A and tensor_B by making their inner dimensions match.\n",
        "\n",
        "- One of the ways to do this is with a transpose (switch the dimensions of a given tensor).\n",
        "\n",
        "- you cannot multiply 3x2 @ 3x2\n",
        "\n",
        "  - instead you transpose one of them\n",
        "    - e.g. 3x2 @ Tranpose(3x2)\n",
        "     which gives 3x2 @ 2x3\n",
        "    - e.g. Tranpose(3x2) @ 3x2\n",
        "     which gives 2x3 @ 3x2"
      ],
      "metadata": {
        "id": "kVxLGDX2-Gik"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ***Tranposes in PyTorch***\n",
        "\n",
        "You can perform transposes in PyTorch using either:\n",
        "\n",
        "- torch.transpose(input, dim0, dim1) - where input is the desired tensor to transpose and dim0 and dim1 are the dimensions to be swapped.\n",
        "\n",
        "- tensor.T - where tensor is the desired tensor to transpose.\n"
      ],
      "metadata": {
        "id": "2185I7xw-Hmz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Lets try tensor.T for Transpose\n",
        "\n",
        "print(tensorA)\n",
        "print()\n",
        "print(tensorB)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ca7MnATJ-HFB",
        "outputId": "e335d92c-b6f0-4dbe-ebc6-bd3efbd3039c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[1., 2.],\n",
            "        [3., 4.],\n",
            "        [5., 6.]])\n",
            "\n",
            "tensor([[ 7.,  8.],\n",
            "        [ 9., 10.],\n",
            "        [11., 12.]])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(tensorA)\n",
        "print()\n",
        "print(tensorB.T)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a1No0fYM8Hql",
        "outputId": "81eabb7f-b6e0-490a-d693-d395ffd9053e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[1., 2.],\n",
            "        [3., 4.],\n",
            "        [5., 6.]])\n",
            "\n",
            "tensor([[ 7.,  9., 11.],\n",
            "        [ 8., 10., 12.]])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# now trying matmul() again along with Transposing tensorB\n",
        "\n",
        "torch.matmul(tensorA,tensorB.T)  # torch.mm() can also be used"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5t49YdUo_Iht",
        "outputId": "c1540bd4-7b3e-4282-cc9f-caabfbdd98b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[ 23.,  29.,  35.],\n",
              "        [ 53.,  67.,  81.],\n",
              "        [ 83., 105., 127.]])"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#torch.matmul(tensorA,tensorB)  # without the .T it won't work"
      ],
      "metadata": {
        "id": "sdGTRcuF_PVN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "you can visualize matrix multiplication at\n",
        "http://matrixmultiplication.xyz/\n",
        "\n",
        "\n",
        "- Neural networks are full of matrix multiplications and dot products.\n",
        "\n",
        "- The torch.nn.Linear() module, also known as a feed-forward layer or fully connected layer, implements a matrix multiplication between an input x and a weights matrix A.\n",
        "   - ```y = x⋅A^T + b```\n",
        "    - where, x is the input to the layer (deep learning is a stack of layers like torch.nn.Linear() and others on top of each other).\n",
        "    - A is the weights matrix created by the layer, this starts out as random numbers that get adjusted as a neural network learns to better represent patterns in the data (notice the \"T\", that's because the weights matrix gets transposed). (Note: You might also often see W or another letter like X used to showcase the weights matrix.)\n",
        "    - b is the bias term used to slightly offset the weights and inputs.\n",
        "    - y is the output (a manipulation of the input in the hopes to discover patterns in it).\n",
        "    - TL;DR:\n",
        "      - x -- we give as i/p\n",
        "      - A -- created & adjusted automatically\n",
        "      - T -- created automatically but remains constant unlike A\n",
        "      - y -- o/p\n",
        "\n",
        " - This is a linear function (you may have seen something like  \n",
        "  y=mx+b in high school or elsewhere), and can be used to draw a straight line!\n",
        "\n",
        "Playing around with the linear layer,\n",
        "changing values in ```in_features``` and ```out_features``` below to see what happens.\n",
        "(Look out for any change in shapes)"
      ],
      "metadata": {
        "id": "tTUm4_8JaX6f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# defining a linear layer (fully connected layer) in a neural network, applying it to an input tensor\n",
        "\n",
        "linear = torch.nn.Linear(in_features=2,  # in_features = matches inner dimension of input\n",
        "                         out_features=6)  # out_features = describes outer value\n",
        "x = tensorA\n",
        "output = linear(x)\n",
        "print(x.shape)\n",
        "print(output)\n",
        "print(output.shape)"
      ],
      "metadata": {
        "id": "GB8tRjVA_puk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "04350e65-a0b7-456f-a0b0-2180455e9981"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([3, 2])\n",
            "tensor([[-0.4852, -0.3243,  0.9533,  1.3539, -1.3560, -0.5534],\n",
            "        [-1.5273,  0.0530,  2.7791,  2.4612, -3.0734, -0.9989],\n",
            "        [-2.5695,  0.4304,  4.6048,  3.5686, -4.7907, -1.4444]],\n",
            "       grad_fn=<AddmmBackward0>)\n",
            "torch.Size([3, 6])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Since the linear layer starts with a random weights matrix, let's make it reproducible\n",
        "\n",
        "torch.manual_seed(42)  # makes it reproducible, random function will create the same input (until you disconnect collab)\n",
        "\n",
        "# this uses matrix multiplication\n",
        "linear = torch.nn.Linear(in_features=2,  # in_features = matches inner dimension of input\n",
        "                         out_features=6)  # out_features = describes outer value\n",
        "x = tensorA\n",
        "output = linear(x)  # this line applies the linear layer to the input tensor\n",
        "print(f\"input shape: {x.shape}\")\n",
        "print()\n",
        "print(f\"output: {output}\")\n",
        "print()\n",
        "print(f\"output shape: {output.shape}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "57bpbIgyZ0ab",
        "outputId": "f6b1eb5f-4296-4da5-aab6-8b53c1400cee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "input shape: torch.Size([3, 2])\n",
            "\n",
            "output: tensor([[2.2368, 1.2292, 0.4714, 0.3864, 0.1309, 0.9838],\n",
            "        [4.4919, 2.1970, 0.4469, 0.5285, 0.3401, 2.4777],\n",
            "        [6.7469, 3.1648, 0.4224, 0.6705, 0.5493, 3.9716]],\n",
            "       grad_fn=<AddmmBackward0>)\n",
            "\n",
            "output shape: torch.Size([3, 6])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "GJEuLtfWZ0U_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ***Finding the min, max, mean, sum, etc (aggregation)***\n",
        "\n",
        "Now we've seen a few ways to manipulate tensors, let's run through a few ways to aggregate them (go from more values to less values).\n",
        "\n",
        "First we'll create a tensor and then find the max, min, mean and sum of it.\n",
        "\n"
      ],
      "metadata": {
        "id": "ko_xKAvaFGWJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating a tensor\n",
        "\n",
        "x = torch.arange(0,100,10)\n",
        "x"
      ],
      "metadata": {
        "id": "q3OmckJrZ0NK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "abb90c4f-dd96-43c5-e42b-e6c7674f5abd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([ 0, 10, 20, 30, 40, 50, 60, 70, 80, 90])"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Performing aggregations\n",
        "\n",
        "print(x.min())\n",
        "print(x.max())\n",
        "print(x.type(torch.float32).mean())  # won't work without float datatype\n",
        "#print(x.mean())  # won't work\n",
        "print(x.sum())\n"
      ],
      "metadata": {
        "id": "cTci5mIFr0Vk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0d93f244-588d-41fd-9942-ecf6520c484c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor(0)\n",
            "tensor(90)\n",
            "tensor(45.)\n",
            "tensor(450)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# same can be done using torch methods\n",
        "\n",
        "torch.min(x), torch.max(x), torch.mean(x.type(torch.float32)), torch.sum(x)  # its .type() and not .dtype()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8831GRzWHC7y",
        "outputId": "aed466bf-a9e2-4c3b-8dd5-7d57f0b85a06"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor(0), tensor(90), tensor(45.), tensor(450))"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "SrDE2xErHCzK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ***Positional min/max***\n",
        "\n",
        "With ```torch.argmin()``` & ```torch.argmax()``` we can find the position of the min and max\n",
        "\n",
        "This is helpful incase you just want the position where the highest (or lowest) value is and not the actual value itself (we'll see this in a later section when using the softmax activation function).\n",
        "\n"
      ],
      "metadata": {
        "id": "hP61I1QeHzVT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating a Tensor\n",
        "\n",
        "xq = torch.arange(10,100,10)\n",
        "print(xq)\n",
        "\n",
        "print(torch.argmin(xq))  # display position of min value\n",
        "print(torch.argmax(xq))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RDlDkYxSHCoK",
        "outputId": "79fe42e0-bb94-452e-e908-37d8a1bb88b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([10, 20, 30, 40, 50, 60, 70, 80, 90])\n",
            "tensor(0)\n",
            "tensor(8)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "m4FBOWn8JMFl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ***Change tensor datatype***\n",
        "\n",
        "As mentioned, a common issue with deep learning operations is having your tensors in different datatypes.\n",
        "\n",
        "If one tensor is in torch.float64 and another is in torch.float32, you might run into some errors.\n",
        "\n",
        "But there's a fix.\n",
        "\n",
        "You can change the datatypes of tensors using\n",
        "```torch.Tensor.type(dtype=None)```\n",
        "\n",
        "where the dtype parameter is the datatype you'd like to use.\n",
        "\n"
      ],
      "metadata": {
        "id": "P35TCdUBJMVT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating a tensor & checking its datatype\n",
        "\n",
        "tensor = torch.arange(10,100,10)\n",
        "tensor.dtype\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MdAxLfj8JL_c",
        "outputId": "f9da3afe-aa72-4a5c-a70d-f5ca47cd1b84"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.int64"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating a float16 tensor using .type()\n",
        "\n",
        "tensorf16 = tensor.type(torch.float16)\n",
        "tensorf16.dtype\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3650ZfP3YUBu",
        "outputId": "24676b3c-b194-458b-b2cd-86c1bfb93fa5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.float16"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating a float32 tensor using .to()\n",
        "\n",
        "tensor = tensor.to(torch.float32)\n",
        "tensor.dtype\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pSgOn6VYYTxn",
        "outputId": "4266ec45-53e1-40cc-b67e-9da63b2a72d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch.float32"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*Note:* Different datatypes can be confusing to begin with. But think of it like this, the lower the number (e.g. 32, 16, 8), the less precise a computer stores the value. And with a lower amount of storage, this generally results in faster computation and a smaller overall model. Mobile-based neural networks often operate with 8-bit integers, smaller and faster to run but less accurate than their float32 counterparts."
      ],
      "metadata": {
        "id": "41qhqERc01Ml"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "D-jEd_oXu8eo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ***Reshaping, stacking, squeezing and unsqueezing***\n",
        "\n",
        "Often times you'll want to reshape or change the dimensions of your tensors without actually changing the values inside them.\n",
        "\n",
        "To do so, some popular methods are:\n",
        "  - torch.reshape(input, shape)\n",
        "    - Reshapes input to shape (if compatible), can also use torch.Tensor.reshape().\n",
        "  - Tensor.view(shape)\n",
        "    - Returns a view of the original tensor in a different shape but shares the same data as the original tensor.\n",
        "  - torch.stack(tensors, dim=0)\n",
        "    - Concatenates a sequence of tensors along a new dimension (dim), all tensors must be same size.\n",
        "  - torch.squeeze(input)\n",
        "    - Squeezes input to remove all the dimenions with value 1.\n",
        "  - torch.unsqueeze(input, dim)\n",
        "    - Returns input with a dimension value of 1 added at dim.\n",
        "  - torch.permute(input, dims)\n",
        "    - Returns a view of the original input with its dimensions permuted (rearranged) to dims.\n",
        "\n",
        "Why do any of these?\n",
        "\n",
        "Because deep learning models (neural networks) are all about manipulating tensors in some way. And because of the rules of matrix multiplication, if you've got shape mismatches, you'll run into errors. These methods help you make sure the right elements of your tensors are mixing with the right elements of other tensors.\n"
      ],
      "metadata": {
        "id": "mbQDRDbyn_Pm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating a tensor\n",
        "import torch\n",
        "x = torch.arange(1, 8)\n",
        "x, x.shape"
      ],
      "metadata": {
        "id": "JhUE1DzlaKhy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9c19c939-d5e3-4631-cb7d-ba56ce037269"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor([1, 2, 3, 4, 5, 6, 7]), torch.Size([7]))"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Adding extra Dimension to the tensor\n",
        "\n",
        "x_reshaped = x.reshape(1,7)\n",
        "x_reshaped, x_reshaped.shape"
      ],
      "metadata": {
        "id": "N7tHhSUaHxu1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b966ecc6-d152-41eb-83cb-7a22120d97ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor([[1, 2, 3, 4, 5, 6, 7]]), torch.Size([1, 7]))"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Changing view (keeps same data as original but changes view)\n",
        "# See more: https://stackoverflow.com/a/54507446/7900723\n",
        "\n",
        "z = x.view(1,7)\n",
        "z, z.shape"
      ],
      "metadata": {
        "id": "cWi88upY0w3l",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3d61505d-58b0-4692-cb65-d3c09dee733a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor([[1, 2, 3, 4, 5, 6, 7]]), torch.Size([1, 7]))"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Changing the view changes the original tensor too\n",
        "\n",
        "z[:, 0] = 5  # changing the value in the 1st column (column index 0) as 5.\n",
        "z, x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Os9PFxKt_v4A",
        "outputId": "787e0a74-be72-4cce-93b5-ad9b96668be0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor([[5, 2, 3, 4, 5, 6, 7]]), tensor([5, 2, 3, 4, 5, 6, 7]))"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "to know more about the z[;, 0] = 5 above\n",
        "check,\n",
        "https://colab.research.google.com/drive/1RqhVKcSHS0_mSzUlHNH6Icdx2IG541zL#scrollTo=IQOvi168DdcX&line=3&uniqifier=1"
      ],
      "metadata": {
        "id": "RYYTx5aDFBHh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Stacking tensors on top of each other\n",
        "\n",
        "x_stacked = torch.stack([x,x,x])  # x_stacked = torch.stack([x,x,x], dim = 0)\n",
        "x_stacked"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3BRLjmmvDX8G",
        "outputId": "bd1f6ffd-0a8d-4ba4-8bb1-d5e1e8985bf9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[5, 2, 3, 4, 5, 6, 7],\n",
              "        [5, 2, 3, 4, 5, 6, 7],\n",
              "        [5, 2, 3, 4, 5, 6, 7]])"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x2_stacked = torch.stack([x,x,x], dim=1)\n",
        "x2_stacked"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r_gC16qyFLSN",
        "outputId": "c3d9fbc1-e2ca-4684-b3fd-379af2e5095a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[5, 5, 5],\n",
              "        [2, 2, 2],\n",
              "        [3, 3, 3],\n",
              "        [4, 4, 4],\n",
              "        [5, 5, 5],\n",
              "        [6, 6, 6],\n",
              "        [7, 7, 7]])"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#x3_stacked = torch.stack([x,x,x], dim=2)  # dim = 2 needs 3d tensor to stack at z-axis & ours is 2d\n",
        "#x3_stacked"
      ],
      "metadata": {
        "id": "wDDkhzcXFK58"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "how the dim in .stack works\n",
        "  - .stack dim = 0  - stacked on top of each other (x axis)\n",
        "  - .stack dim = 1  - stacked next to each other (y axis)\n",
        "  - .stack dim = 2  - stacks at z axis if tensor is 3d"
      ],
      "metadata": {
        "id": "XM0fzHM1Ivh0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "to remove a dimension from the Tensor use .squeeze()"
      ],
      "metadata": {
        "id": "j2-uFGwK9A9T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Squeezing a Tensor\n",
        "\n",
        "x = torch.arange(1,8)  # creating a range of tensor\n",
        "print(x)\n",
        "print(x.shape)\n",
        "print()\n",
        "\n",
        "x_reshaped = torch.reshape(x, (1,7))  # reshaping it (adding another dimension)\n",
        "print(x_reshaped)\n",
        "print(x_reshaped.shape)\n",
        "print()\n",
        "\n",
        "x_squeezed = x_reshaped.squeeze()  # removing a dimension\n",
        "print(x_squeezed)\n",
        "print(x_squeezed.shape)\n"
      ],
      "metadata": {
        "id": "H5zsekpN8JNc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4b69f130-c9f5-4b8e-da0b-81fe3fb3aafc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([1, 2, 3, 4, 5, 6, 7])\n",
            "torch.Size([7])\n",
            "\n",
            "tensor([[1, 2, 3, 4, 5, 6, 7]])\n",
            "torch.Size([1, 7])\n",
            "\n",
            "tensor([1, 2, 3, 4, 5, 6, 7])\n",
            "torch.Size([7])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Unsqueezing a Tensor\n",
        "\n",
        "print(x_squeezed)\n",
        "print()\n",
        "\n",
        "x_unsqueezed = x_squeezed.unsqueeze(dim=0)  # adding another dimension (tbh I would prefer other ways like reshape)\n",
        "print(x_unsqueezed)\n",
        "print(x_unsqueezed.shape)\n"
      ],
      "metadata": {
        "id": "udQInlzv8JI1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ed8c8306-3a9c-4a55-8213-5ce44024c914"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([1, 2, 3, 4, 5, 6, 7])\n",
            "\n",
            "tensor([[1, 2, 3, 4, 5, 6, 7]])\n",
            "torch.Size([1, 7])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Permuting a Tensor\n",
        "\n",
        "x = torch.rand(size=(224, 121, 3))\n",
        "\n",
        "x_permuted = x.permute(2, 0 ,1)  # shifts axis 0->1, 1->2, 2->0\n",
        "\n",
        "print(x.shape)\n",
        "print()\n",
        "print(x_permuted.shape)"
      ],
      "metadata": {
        "id": "GXGhPiayIurB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "236c8c31-01be-46bd-c965-3fe56404bc24"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch.Size([224, 121, 3])\n",
            "\n",
            "torch.Size([3, 224, 121])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "You can also rearrange the order of axes values with torch.permute(input, dims), where the input gets turned into a view with new dims."
      ],
      "metadata": {
        "id": "CuMfFpdxa5IF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "***Note:*** Because permuting returns a view (shares the same data as the original), the values in the permuted tensor will be the same as the original tensor and if you change the values in the view, it will change the values of the original."
      ],
      "metadata": {
        "id": "9LqiV9R6bqdJ"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "f5CaY5NqIunp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ***Indexing (selecting data from tensors)***\n",
        "\n",
        "Sometimes you'll want to select specific data from tensors (for example, only the first column or second row).\n",
        "\n",
        "To do so, you can use indexing. It is similar to Python & Numpy array Indexing\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "4Z1HcCKcbuW4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating a Tensor\n",
        "\n",
        "x = torch.randint(1,100,(1,3,3))\n",
        "x"
      ],
      "metadata": {
        "id": "5rGiDYjwIuk3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "496b22af-a768-44af-91a1-ab1a199baf3f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[[23, 29, 29],\n",
              "         [98, 51, 70],\n",
              "         [27, 76, 88]]])"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's index bracket by bracket\n",
        "print(f\"First square bracket:\\n{x[0]}\")\n",
        "print(f\"Second square bracket: {x[0][0]}\")\n",
        "print(f\"Third square bracket: {x[0][0][0]}\")"
      ],
      "metadata": {
        "id": "LSWEdnNkIuht",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "de521274-cdd5-4128-da9b-660cb6940173"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "First square bracket:\n",
            "tensor([[93, 52, 79],\n",
            "        [80, 23, 16],\n",
            "        [49, 75, 27]])\n",
            "Second square bracket: tensor([93, 52, 79])\n",
            "Third square bracket: 93\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x[0, 0 ,0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8HYU7Q-zgY_Y",
        "outputId": "3dcbad1a-1336-43e0-85b8-b9bd48a99746"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor(93)"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x[:, 0, 0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ltMyU0QohQiz",
        "outputId": "95e7a437-a4f4-4fed-df3c-984151ceb479"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([93])"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "there are 2 dimensions in x, i.e. \"[[ ]]\"\n",
        "\n",
        "x[0, 0, 0] - gives 0th dimension's [0][0] index, so output won't have any \"[ ]\"\n",
        "\n",
        "x[:, 0, 0] - gives 1st dimension's [0][0] index, so output will have a \"[ ]\""
      ],
      "metadata": {
        "id": "ba6x9dDDhbmP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Get all values of 0th dimension and the 0 index of 1st dimension\n",
        "x[:, 0]  # 1st row (row index 0)  & all dimensions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DAW7xCYvfThI",
        "outputId": "27b93f1e-1252-48cc-ceb8-0423fd4a78d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[93, 52, 79]])"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get all values of 0th & 1st dimensions but only index 1 of 2nd dimension\n",
        "x[:, :, 1]  # 2nd columns (column index 1)  & all dimensions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LrIK_T_2fTZC",
        "outputId": "08a195b2-d976-49d4-89a8-1001e97d4704"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[52, 23, 75]])"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get all values of the 0 dimension but only the 1 index value of the 1st and 2nd dimension\n",
        "x[:, 1, 1]  #  [1][1]"
      ],
      "metadata": {
        "id": "Um2-Zkz-IuZR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "96cb001c-dece-49dc-adaf-d4a26f2b56e4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([23])"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bL4rZGiFiyzE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# try the above with actual 3D tensor\n",
        "\n",
        "tensor_3d = torch.tensor([[[1, 2, 3],\n",
        "                                [4, 5, 6],\n",
        "                                [7, 8, 9]],\n",
        "                               [[10, 11, 12],\n",
        "                                [13, 14, 15],\n",
        "                                [16, 17, 18]],\n",
        "                               [[19, 20, 21],\n",
        "                                [22, 23, 24],\n",
        "                                [25, 26, 27]]])"
      ],
      "metadata": {
        "id": "n362un22ki2M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Pno-QFkmkiy0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ***Indexing and Modifying a whole column/row in a Tensor***\n",
        "\n"
      ],
      "metadata": {
        "id": "IQOvi168DdcX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating a Tensor\n",
        "\n",
        "t = torch.randint(0, 22, (3,3))\n",
        "t"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "noLWKVPa6faT",
        "outputId": "8ce48b77-2ba1-4598-9aa4-40e0dc4a84ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[ 0,  3, 11],\n",
              "        [12,  1,  2],\n",
              "        [14,  7, 18]])"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Changing values of a whole row\n",
        "\n",
        "t[2, :] = 52  # this changes all values in 3rd row (row index 2) as 52\n",
        "t"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hA6ckhpR_kZa",
        "outputId": "c4a24aae-9fac-446a-f693-0e6fa298a4a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[ 0,  3, 11],\n",
              "        [12,  1,  2],\n",
              "        [52, 52, 52]])"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Changing values of a whole column\n",
        "\n",
        "t = torch.randint(0, 22, (3,3))\n",
        "\n",
        "print(t)\n",
        "print()\n",
        "\n",
        "t[:, 1] = 76  # this changes all values in 2nd column (column index 1) as 76\n",
        "\n",
        "print(t)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rsGfr6gl_kI0",
        "outputId": "935eac41-b854-4509-f76f-a545b08a317a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[19, 12,  9],\n",
            "        [15, 19,  6],\n",
            "        [19, 16,  3]])\n",
            "\n",
            "tensor([[19, 76,  9],\n",
            "        [15, 76,  6],\n",
            "        [19, 76,  3]])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# # Changing values of a whole Tensor\n",
        "\n",
        "t = torch.randint(0, 22, (3,3))\n",
        "\n",
        "print(t)\n",
        "print()\n",
        "\n",
        "t[:, :] = 98  # this changes all values as 98\n",
        "\n",
        "print(t)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nk1g9Wzj6fQb",
        "outputId": "e6f33278-57b9-422b-c841-1ca9f46d0fc5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[15, 14, 11],\n",
            "        [15,  6, 13],\n",
            "        [15, 14, 19]])\n",
            "\n",
            "tensor([[98, 98, 98],\n",
            "        [98, 98, 98],\n",
            "        [98, 98, 98]])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Cy-xNUYaBVFw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ***Reproducibility (trying to take the random out of random)***\n",
        "\n",
        "As you learn more about neural networks and machine learning, you'll start to discover how much randomness plays a part.\n",
        "\n",
        "Well, pseudorandomness that is. Because after all, as they're designed, a computer is fundamentally deterministic (each step is predictable) so the randomness they create are simulated randomness\n",
        "\n",
        "- How does this relate to neural networks and deep learning then?\n",
        "\n",
        "We've discussed neural networks start with random numbers to describe patterns in data (these numbers are poor descriptions) and try to improve those random numbers using tensor operations (and a few other things we haven't discussed yet) to better describe patterns in data.\n",
        "\n",
        "In short:\n",
        "\n",
        "start with random numbers -> tensor operations -> try to make better (again and again and again)\n",
        "\n",
        "- Although randomness is nice and powerful, sometimes you'd like there to be a little less randomness.\n",
        "\n",
        "Why?\n",
        "\n",
        "So you can perform repeatable experiments.\n",
        "\n",
        "For example, you create an algorithm capable of achieving X performance.\n",
        "\n",
        "And then your friend tries it out to verify you're not crazy.\n",
        "\n",
        "How could they do such a thing?\n",
        "\n",
        "That's where reproducibility comes in.\n",
        "\n",
        "- In other words, can you get the same (or very similar) results on your computer running the same code as I get on mine?\n",
        "\n",
        "Let's see a brief example of reproducibility in PyTorch.\n",
        "\n",
        "We'll start by creating two random tensors, since they're random, you'd expect them to be different right?\n"
      ],
      "metadata": {
        "id": "z3ryogfIAk8L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating two random tensors and comparing whether they are equal\n",
        "tensor_a = torch.rand(3,4)\n",
        "tensor_b = torch.rand(3,4)\n",
        "\n",
        "tensor_a, tensor_b, tensor_a == tensor_b\n"
      ],
      "metadata": {
        "id": "47FXuqFP8G4w",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7e5e8179-46ab-4152-f3a8-67d3b67e2723"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor([[0.5779, 0.9040, 0.5547, 0.3423],\n",
              "         [0.6343, 0.3644, 0.7104, 0.9464],\n",
              "         [0.7890, 0.2814, 0.7886, 0.5895]]),\n",
              " tensor([[0.7539, 0.1952, 0.0050, 0.3068],\n",
              "         [0.1165, 0.9103, 0.6440, 0.7071],\n",
              "         [0.6581, 0.4913, 0.8913, 0.1447]]),\n",
              " tensor([[False, False, False, False],\n",
              "         [False, False, False, False],\n",
              "         [False, False, False, False]]))"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Just as you might've expected, the tensors come out with different values.\n",
        "\n",
        "But what if you wanted to created two random tensors with the same values.\n",
        "\n",
        "As in, the tensors would still contain random values but they would be of the same flavour.\n",
        "\n",
        "That's where torch.manual_seed(seed) comes in, where seed is an integer (like 42 but it could be anything) that flavours the randomness."
      ],
      "metadata": {
        "id": "yZ4LR0OlzZ0e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating pseudo random tensors\n",
        "\n",
        "import random\n",
        "\n",
        "torch.manual_seed(seed=42)\n",
        "tensor_c = torch.rand(3,4)\n",
        "\n",
        "torch.manual_seed(seed=42)\n",
        "tensor_d = torch.rand(3,4)\n",
        "\n",
        "tensor_c, tensor_d, tensor_c == tensor_d"
      ],
      "metadata": {
        "id": "dyWN85eW8Gwa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1896b40f-6e70-4be0-f84c-7a538eb6917c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(tensor([[0.8823, 0.9150, 0.3829, 0.9593],\n",
              "         [0.3904, 0.6009, 0.2566, 0.7936],\n",
              "         [0.9408, 0.1332, 0.9346, 0.5936]]),\n",
              " tensor([[0.8823, 0.9150, 0.3829, 0.9593],\n",
              "         [0.3904, 0.6009, 0.2566, 0.7936],\n",
              "         [0.9408, 0.1332, 0.9346, 0.5936]]),\n",
              " tensor([[True, True, True, True],\n",
              "         [True, True, True, True],\n",
              "         [True, True, True, True]]))"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "GSgOsrqfBrRv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "More about the .view\n",
        "\n",
        "- Using .view() creates a new view of the tensor's data, and modifications to the view affect the original tensor and vice versa.\n",
        "- Simple assignment without .view() creates another reference to the same tensor data, and modifications to one reference affect the other."
      ],
      "metadata": {
        "id": "Z73Y_VOh6Lf0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###***Common Note :***\n",
        "\n",
        "%%time -- to find the time taken for execution\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "9GclB0TKrqCH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Quick code note\n",
        "\n",
        "# to generate a random integer tensor 3x3\n",
        "x = torch.randint(1,100,(3,3))\n",
        "\n",
        "# to generate a random integer tensor with dimension\n",
        "x = torch.randint(1,100,(3,3)).reshape(1,3,3)  # 1D 3x3\n",
        "\n",
        "\n",
        "# to generate a range of tensor\n",
        "x = torch.arange(1,8)\n",
        "\n",
        "# to generate a range of tensor with dimension\n",
        "x = torch.arange(1,10).reshape(1,3,3)  # 1D 3x3\n"
      ],
      "metadata": {
        "id": "UUIARfiW6Onm"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}