nn.Fold expects an input of [batch_size, channels * prod(kernel_size), number of blocks] to create a 4D output.
The unfold and fold operations are reversible, if you don’t have overlapping patches as seen here:
fold_params = dict(kernel_size=2, dilation=1, padding=0, stride=2)
fold = nn.Fold(output_size=(16, 16), **fold_params)
unfold = nn.Unfold(**fold_params)

x = torch.arange(16*16).float().view(1, 1, 16, 16)
y = unfold(x)
z = fold(y)

print((x - z).abs().sum())
> tensor(0.)

Based on the provided code I guess you should unfold ht before passing it to the new layer.