Strange behavior of nn.Padding during training

[tastyminerals]

I have a standard training loop in a sequential recurrent model. The model is trained with two input batches of data where:

x1 batch (25 x 32)
x2 batch (25 x 32 x 9 x 1)

The last batches in the epoch are smaller and are:

x1 last batch (14 x 32)
x2 last batch (14 x 32 x 9 x 1)

So, I use nn.Padding to add more dimensions for the last batches to make them always have the same sequence length like so:

while opt.maxepoch <= 0 or epoch <= opt.maxepoch do
  print("Epoch #"..epoch)
  local pad1 = nn.Padding(0, 11, 0)
  local pad2 = nn.Padding(0, 11, 0)
  local pad3 = nn.Padding(0, 11, 0)

  for batch1, batch2, batch3 in utils.zip(words_it, feats_it, targets_it) do
    local i, wvec, _ = unpack(batch1) -- word idx
    local _, fvec, _ = unpack(batch2) -- feat vec
    local _, gt, _ = unpack(batch3) -- targets [0,1]

    if wvec:size()[1] ~= opt.seqlen then
      wvec = pad1:forward(wvec)
      fvec = pad2:forward(fvec)
      gt = pad2:forward(gt)
      print({fvec}) -- prints {CudaTensor - size: 25x32}, should be {CudaTensor - size: 25x32x9}
    end


  local outputs = model:forward({wvec, fvec:squeeze(4), gt})
    -- (...)
  end
end

However, pad2:forward(fvec) returns 25x32 instead of 25x32x9x1 all the time.
When I use nn.Padding only for fvec tensor it returns correct 25x32x9x1 which is bewildering.
How does this happen?