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Instead of doing what's said in the example, you can do:
import torch
from perceiver_pytorch import Perceiver
model = Perceiver(
input_channels = 2, # or 1 if you have only one channel
input_axis = 3, # 3 because we have a volume
)
vol = torch.randn(1, 64, 64, 64, 2) # a volume in a channel-last format
model(vol)
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