The shapes of the tensors you wish to concatenate do not match.
print(out1.shape)
> torch.Size([1, 4096])
print(y.shape)
> torch.Size([1, 96, 4, 4])
print(z.shape)
> torch.Size([1, 96, 2, 2])

Based on the image you’ve posted it seems the conv activations should be flattened to a tensor with the shape [batch_size, 2 * 4*4*96 = 3072].
You could add this using:
y = y.view(y.size(0), -1)
z = z.view(y.size(0), -1)
out = torch.cat((out1, y, z), 1)

However, even then the architecture won’t match, since s is only [batch_size, 96, 2, 2].