Yes that should be possible, if you are able to create a view of the tensor in the expected shape.
nn.ConvTranspose2d expects an input in the shape [batch_size, channels, height, width].
Since your linear layer is returning 100 output features, you won’t be able to use in_channels=128, but would have to lower it.
You could use out = out.view(out.size(0), 4, 5, 5) on the output of the linear layer and pass it to the transposed conv.