def _sliding_chunks_query_key_matmul(self, query, key, window_overlap):
        """
        Matrix multiplication of query and key tensors using with a sliding window attention pattern. This
        implementation splits the input into overlapping chunks of size 2w (e.g. 512 for pretrained Longformer) with an
        overlap of size window_overlap
        """
        batch_size, seq_len, num_heads, head_dim = shape_list(query)

        tf.debugging.assert_equal(
            seq_len % (window_overlap * 2),
            0,
            message=f"Sequence length should be multiple of {window_overlap * 2}. Given {seq_len}",
        )
        tf.debugging.assert_equal(
            shape_list(query),
            shape_list(key),
            message=(
                f"Shape of query and key should be equal, but got query: {shape_list(query)} and key:"
                f" {shape_list(key)}"
            ),
        )

        chunks_count = seq_len // window_overlap - 1

        # group batch_size and num_heads dimensions into one, then chunk seq_len into chunks of size window_overlap * 2
        query = tf.reshape(
            tf.transpose(query, (0, 2, 1, 3)),
            (batch_size * num_heads, seq_len, head_dim),
        )
        key = tf.reshape(tf.transpose(key, (0, 2, 1, 3)), (batch_size * num_heads, seq_len, head_dim))
        chunked_query = self._chunk(query, window_overlap)
        chunked_key = self._chunk(key, window_overlap)

        # matrix multiplication
        # bcxd: batch_size * num_heads x chunks x 2window_overlap x head_dim
        # bcyd: batch_size * num_heads x chunks x 2window_overlap x head_dim
        # bcxy: batch_size * num_heads x chunks x 2window_overlap x 2window_overlap
        chunked_query = tf.cast(chunked_query, dtype=chunked_key.dtype)
        chunked_attention_scores = tf.einsum("bcxd,bcyd->bcxy", chunked_query, chunked_key)  # multiply

        # convert diagonals into columns
        paddings = tf.convert_to_tensor([[0, 0], [0, 0], [0, 1], [0, 0]])
        diagonal_chunked_attention_scores = self._pad_and_transpose_last_two_dims(chunked_attention_scores, paddings)

        # allocate space for the overall attention matrix where the chunks are combined. The last dimension
        # has (window_overlap * 2 + 1) columns. The first (window_overlap) columns are the window_overlap lower triangles (attention from a word to
        # window_overlap previous words). The following column is attention score from each word to itself, then
        # followed by window_overlap columns for the upper triangle.

        # copy parts from diagonal_chunked_attention_scores into the combined matrix of attentions
        # - copying the main diagonal and the upper triangle
        # TODO: This code is most likely not very efficient and should be improved
        diagonal_attn_scores_up_triang = tf.concat(
            [
                diagonal_chunked_attention_scores[:, :, :window_overlap, : window_overlap + 1],
                diagonal_chunked_attention_scores[:, -1:, window_overlap:, : window_overlap + 1],
            ],
            axis=1,
        )

        # - copying the lower triangle
        diagonal_attn_scores_low_triang = tf.concat(
            [
                tf.zeros(
                    (batch_size * num_heads, 1, window_overlap, window_overlap),
                    dtype=diagonal_chunked_attention_scores.dtype,
                ),
                diagonal_chunked_attention_scores[:, :, -(window_overlap + 1) : -1, window_overlap + 1 :],
            ],
            axis=1,
        )
        diagonal_attn_scores_first_chunk = tf.concat(
            [
                tf.roll(
                    diagonal_chunked_attention_scores,
                    shift=[1, window_overlap],
                    axis=[2, 3],
                )[:, :, :window_overlap, :window_overlap],
                tf.zeros(
                    (batch_size * num_heads, 1, window_overlap, window_overlap),
                    dtype=diagonal_chunked_attention_scores.dtype,
                ),
            ],
            axis=1,
        )
        first_chunk_mask = (
            tf.tile(
                tf.range(chunks_count + 1, dtype=tf.int64)[None, :, None, None],
                (batch_size * num_heads, 1, window_overlap, window_overlap),
            )
            < 1
        )
        diagonal_attn_scores_low_triang = tf.where(
            first_chunk_mask,
            diagonal_attn_scores_first_chunk,
            diagonal_attn_scores_low_triang,
        )

        # merging upper and lower triangle
        diagonal_attention_scores = tf.concat(
            [diagonal_attn_scores_low_triang, diagonal_attn_scores_up_triang], axis=-1
        )

        # separate batch_size and num_heads dimensions again
        diagonal_attention_scores = tf.transpose(
            tf.reshape(
                diagonal_attention_scores,
                (batch_size, num_heads, seq_len, 2 * window_overlap + 1),
            ),
            (0, 2, 1, 3),
        )

        diagonal_attention_scores = self._mask_invalid_locations(diagonal_attention_scores, window_overlap)

        return diagonal_attention_scores

        Matrix multiplication of query and key tensors using with a sliding window attention pattern. This
        implementation splits the input into overlapping chunks of size 2w (e.g. 512 for pretrained Longformer) with an
        overlap of size window_overlap
        
dpkd/transformers/src/transformers/models/led/modeling_tf_led.py