Is there something like model.summary() in pyTorch? I couldn’t find it on the documentation. I tried model.type, which gives you something like (see below). But I am missing the info on the shape of each output, like (None, 28, 28, 512) or similar. Any ideas?

```
(main): Sequential (
(initial-100.512.convt): ConvTranspose2d(100, 512, kernel_size=(4, 4), stride=(1, 1), bias=False)
(initial-512.batchnorm): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True)
(initial-512.relu): ReLU (inplace)
(pyramid-512.256.convt): ConvTranspose2d(512, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(pyramid-256.batchnorm): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True)
(pyramid-256.relu): ReLU (inplace)
(pyramid-256.128.convt): ConvTranspose2d(256, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(pyramid-128.batchnorm): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True)
(pyramid-128.relu): ReLU (inplace)
(pyramid-128.64.convt): ConvTranspose2d(128, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(pyramid-64.batchnorm): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)
(pyramid-64.relu): ReLU (inplace)
(extra-0-64.64.convt): ConvTranspose2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
(extra-0-64.batchnorm): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True)
(extra-0-64.relu): ReLU (inplace)
(final.64-3.convt): ConvTranspose2d(64, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)
(final.3.tanh): Tanh ()
)
)>
```