code: def masked_flip(padded_sequence: torch.Tensor,
                  sequence_lengths: List[int]) -> torch.Tensor:
      
      assert padded_sequence.size(0) == len(sequence_lengths), \
          f'sequence_lengths length ${len(sequence_lengths)} does not match batch size ${padded_sequence.size(0)}'
      num_timesteps = padded_sequence.size(1)
      flipped_padded_sequence = torch.flip(padded_sequence, [1])
      sequences = [flipped_padded_sequence[i, num_timesteps - length:] for i, length in enumerate(sequence_lengths)]
      return torch.nn.utils.rnn.pad_sequence(sequences, batch_first=True)
docs: Flips a padded tensor along the time dimension without affecting masked entries.
  
          Parameters
          ----------
          padded_sequence : ``torch.Tensor``
              The tensor to flip along the time dimension.
              Assumed to be of dimensions (batch size, num timesteps, ...)
          sequence_lengths : ``torch.Tensor``
              A list containing the lengths of each unpadded sequence in the batch.
  
          Returns
          -------
          A ``torch.Tensor`` of the same shape as padded_sequence.