    def collater(self, samples, pad_to_length=None):
        """Merge a list of samples to form a mini-batch.

        Args:
            samples (List[dict]): samples to collate
            pad_to_length (dict, optional): a dictionary of
                {'source': source_pad_to_length, 'target': target_pad_to_length}
                to indicate the max length to pad to in source and target respectively.

        Returns:
            dict: a mini-batch with the following keys:

                - `id` (LongTensor): example IDs in the original input order
                - `ntokens` (int): total number of tokens in the batch
                - `net_input` (dict): the input to the Model, containing keys:

                  - `src_tokens` (LongTensor): a padded 2D Tensor of tokens in
                    the source sentence of shape `(bsz, src_len)`. Padding will
                    appear on the left if *left_pad_source* is ``True``.
                  - `src_lengths` (LongTensor): 1D Tensor of the unpadded
                    lengths of each source sentence of shape `(bsz)`
                  - `prev_output_tokens` (LongTensor): a padded 2D Tensor of
                    tokens in the target sentence, shifted right by one
                    position for teacher forcing, of shape `(bsz, tgt_len)`.
                    This key will not be present if *input_feeding* is
                    ``False``.  Padding will appear on the left if
                    *left_pad_target* is ``True``.
                  - `src_lang_id` (LongTensor): a long Tensor which contains source
                    language IDs of each sample in the batch

                - `target` (LongTensor): a padded 2D Tensor of tokens in the
                  target sentence of shape `(bsz, tgt_len)`. Padding will appear
                  on the left if *left_pad_target* is ``True``.
                - `tgt_lang_id` (LongTensor): a long Tensor which contains target language
                   IDs of each sample in the batch
        """
        res = collate(
            samples,
            pad_idx=self.src_dict.pad(),
            eos_idx=self.eos,
            left_pad_source=self.left_pad_source,
            left_pad_target=self.left_pad_target,
            input_feeding=self.input_feeding,
            pad_to_length=pad_to_length,
            pad_to_multiple=self.pad_to_multiple,
        )
        if self.src_lang_id is not None or self.tgt_lang_id is not None:
            src_tokens = res["net_input"]["src_tokens"]
            bsz = src_tokens.size(0)
            if self.src_lang_id is not None:
                res["net_input"]["src_lang_id"] = (
                    torch.LongTensor([[self.src_lang_id]]).expand(bsz, 1).to(src_tokens)
                )
            if self.tgt_lang_id is not None:
                res["tgt_lang_id"] = (
                    torch.LongTensor([[self.tgt_lang_id]]).expand(bsz, 1).to(src_tokens)
                )
        return res
Merge a list of samples to form a mini-batch.

Args:
samples (List[dict]): samples to collate
pad_to_length (dict, optional): a dictionary of
{'source': source_pad_to_length, 'target': target_pad_to_length}
to indicate the max length to pad to in source and target respectively.

Returns:
dict: a mini-batch with the following keys:

- `id` (LongTensor): example IDs in the original input order
- `ntokens` (int): total number of tokens in the batch
- `net_input` (dict): the input to the Model, containing keys:

- `src_tokens` (LongTensor): a padded 2D Tensor of tokens in
the source sentence of shape `(bsz, src_len)`. Padding will
appear on the left if *left_pad_source* is ``True``.
- `src_lengths` (LongTensor): 1D Tensor of the unpadded
lengths of each source sentence of shape `(bsz)`
- `prev_output_tokens` (LongTensor): a padded 2D Tensor of
tokens in the target sentence, shifted right by one
position for teacher forcing, of shape `(bsz, tgt_len)`.
This key will not be present if *input_feeding* is
``False``.  Padding will appear on the left if
*left_pad_target* is ``True``.
- `src_lang_id` (LongTensor): a long Tensor which contains source
language IDs of each sample in the batch

- `target` (LongTensor): a padded 2D Tensor of tokens in the
target sentence of shape `(bsz, tgt_len)`. Padding will appear
on the left if *left_pad_target* is ``True``.
- `tgt_lang_id` (LongTensor): a long Tensor which contains target language
IDs of each sample in the batch
fairseq/data/language_pair_dataset.py