def masked_flip(padded_sequence: torch.Tensor, sequence_lengths: List[int]) -> torch.Tensor:
    """
    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

    `torch.Tensor`
        A `torch.Tensor` of the same shape as padded_sequence.
    """
    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)
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

`torch.Tensor`
A `torch.Tensor` of the same shape as padded_sequence.
allennlp/nn/util.py