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import numpy as np
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from sklearn .model_selection import train_test_split
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import torch .optim .lr_scheduler as lr_scheduler
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- from tnlearn .tnlearn . utils import MyData
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- from tnlearn .tnlearn . neurons import CustomNeuronLayer
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- from tnlearn .tnlearn . utils import random_seed
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- from tnlearn .tnlearn . utils import get_activation_function
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- from tnlearn .tnlearn . utils import get_loss_function
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- from tnlearn .tnlearn . utils import get_optimizer
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- from tnlearn .tnlearn . base import BaseModel
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+ from tnlearn .utils import MyData
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+ from tnlearn .neurons import CustomNeuronLayer
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+ from tnlearn .utils import random_seed
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+ from tnlearn .utils import get_activation_function
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+ from tnlearn .utils import get_loss_function
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+ from tnlearn .utils import get_optimizer
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+ from tnlearn .base import BaseModel
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class MLPRegressor (BaseModel ):
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