def batch_by_size(
        self,
        indices,
        max_tokens=None,
        max_sentences=None,
        required_batch_size_multiple=1,
    ):
        """
        Given an ordered set of indices, return batches according to
        *max_tokens*, *max_sentences* and *required_batch_size_multiple*.
        """
        from fairseq.data import data_utils

        fixed_shapes = self.get_batch_shapes()
        if fixed_shapes is not None:

            def adjust_bsz(bsz, num_tokens):
                if bsz is None:
                    assert max_tokens is not None, "Must specify --max-tokens"
                    bsz = max_tokens // num_tokens
                if max_sentences is not None:
                    bsz = min(bsz, max_sentences)
                elif (
                    bsz >= required_batch_size_multiple
                    and bsz % required_batch_size_multiple != 0
                ):
                    bsz -= bsz % required_batch_size_multiple
                return bsz

            fixed_shapes = np.array(
                [
                    [adjust_bsz(bsz, num_tokens), num_tokens]
                    for (bsz, num_tokens) in fixed_shapes
                ]
            )

        try:
            num_tokens_vec = self.num_tokens_vec(indices).astype("int64")
        except NotImplementedError:
            num_tokens_vec = None

        return data_utils.batch_by_size(
            indices,
            num_tokens_fn=self.num_tokens,
            num_tokens_vec=num_tokens_vec,
            max_tokens=max_tokens,
            max_sentences=max_sentences,
            required_batch_size_multiple=required_batch_size_multiple,
            fixed_shapes=fixed_shapes,
        )

        Given an ordered set of indices, return batches according to
        *max_tokens*, *max_sentences* and *required_batch_size_multiple*.
        
understand_icl/fairseq/fairseq/data/fairseq_dataset.py