I wrote a debugging environment. Its observation space is one number in the range of (1,12). Each step the next number is returned as observation. The episode is done, once the end of this range is reached.
After sampling observations, I padded and packed the data. I left a copy of the observations untouched for comparison.
This is the output of the packed_padded_sequence:
[ 9.,  1.,  3., 11.,  5.,  7., 10.,  6.,  8.,  2.,  4.,  0.,  5.,  0.,
         0.,  7.,  1.,  3.,  6.,  2.,  4.,  0.,  0.,  1.,  9., 11.,  3.,  0.,
         0.,  2.,  0.,  0., 10.,  0.,  0.,  5.,  7.,  0.,  9., 11.,  9.,  1.,
         3., 11.,  5.,  7.,  5.,  0.,  0.,  7.,  1.,  3.,  1.,  9., 11.,  3.,
         0.,  0.,  0.,  5.,  7.,  0.,  9.,  0.]

And this is what I expected:
[ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11.,  1.,  2.,  3.,
         4.,  5.,  6.,  7.,  8.,  9., 10., 11.,  1.,  2.,  3.,  4.,  5.,  6.,
         7.,  8.,  9., 10., 11.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9.,
        10., 11.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11.,  1.,
         2.,  3.,  4.,  5.,  6.,  7.,  8.,  9.]

Two major conflicting observations that can be made:

There are zeros in the data?!
The data is not in order

Before packing the data, I call these lines:
h_shape = tuple(h.size())
h = h.view(sequence_length, (h_shape[0] // sequence_length), h_shape[1])
# Convert data to packed padded sequence
packed = pack_padded_sequence(h, actual_sequence_lengths, enforce_sorted=False)
print(packed.data.view(-1))

The reshaping of the data is done, because the data is flattened in order to feed an entire batch to the first layers of the model.
Why do you guys think are zeros in the packed_data.data ?
Concerning the order I could imagine that the call of the view function does not work like the reshape function in PyTorch.