It might come from somewhere else in your model, since the input data shape and layer you give produces a [2, 128, 65] shaped output:
>>> pool=nn.MaxPool1d(22,stride=1,padding=10,dilation=1)
>>> t = torch.rand(2,128,66)
>>> pool(t).shape
torch.Size([2, 128, 65])




 chaslie:

model(*x)


What is x here?