code: def split_einsum_v2(q, k, v, mask, heads, dim_head):
      """Attention Implementation backing AttentionImplementations.SPLIT_EINSUM_V2
  
      - Implements https://machinelearning.apple.com/research/neural-engine-transformers
      - Recommended for ANE
      - Marginally slower on GPU
      - Chunks the query sequence to avoid large intermediate tensors and improves ANE performance
      """
      query_seq_length = q.size(3)
      num_chunks = query_seq_length // CHUNK_SIZE
  
      if num_chunks == 0:
          logger.info(
              "AttentionImplementations.SPLIT_EINSUM_V2: query sequence too short to chunk "
              f"({query_seq_length}<{CHUNK_SIZE}), fall back to AttentionImplementations.SPLIT_EINSUM (safe to ignore)"
          )
          return split_einsum(q, k, v, mask, heads, dim_head)
  
      logger.info(
          "AttentionImplementations.SPLIT_EINSUM_V2: Splitting query sequence length of "
          f"{query_seq_length} into {num_chunks} chunks"
      )
  
      mh_q = [
          q[:, head_idx * dim_head : (head_idx + 1) * dim_head, :, :]
          for head_idx in range(heads)
      ]  # (bs, dim_head, 1, max_seq_length) * heads
  
      # Chunk the query sequence for each head
      mh_q_chunked = [
          [
              h_q[
                  ...,
                  chunk_idx * CHUNK_SIZE : (chunk_idx + 1) * CHUNK_SIZE,
              ]
              for chunk_idx in range(num_chunks)
          ]
          for h_q in mh_q
      ]  # ((bs, dim_head, 1, QUERY_SEQ_CHUNK_SIZE) * num_chunks) * heads
  
      k = k.transpose(1, 3)
      mh_k = [
          k[:, :, :, head_idx * dim_head : (head_idx + 1) * dim_head]
          for head_idx in range(heads)
      ]  # (bs, max_seq_length, 1, dim_head) * heads
  
      mh_v = [
          v[:, head_idx * dim_head : (head_idx + 1) * dim_head, :, :]
          for head_idx in range(heads)
      ]  # (bs, dim_head, 1, max_seq_length) * heads
  
      attn_weights = [
          [
              torch.einsum("bchq,bkhc->bkhq", [qi_chunk, ki])
              * (dim_head**-0.5)
              for qi_chunk in h_q_chunked
          ]
          for h_q_chunked, ki in zip(mh_q_chunked, mh_k)
      ]  # ((bs, max_seq_length, 1, chunk_size) * num_chunks) * heads
  
      attn_weights = [
          [aw_chunk.softmax(dim=1) for aw_chunk in aw_chunked]
          for aw_chunked in attn_weights
      ]  # ((bs, max_seq_length, 1, chunk_size) * num_chunks) * heads
  
      attn = [
          [
              torch.einsum("bkhq,bchk->bchq", wi_chunk, vi)
              for wi_chunk in wi_chunked
          ]
          for wi_chunked, vi in zip(attn_weights, mh_v)
      ]  # ((bs, dim_head, 1, chunk_size) * num_chunks) * heads
  
      attn = torch.cat(
          [torch.cat(attn_chunked, dim=3) for attn_chunked in attn],
          dim=1,
      )  # (bs, dim, 1, max_seq_length)
  
      return attn
  
imports: import logging
  import torch
label_desc: Use image node_id 2 for calling a global function with example usage: split_einsum_v2(q, k, v, mask, heads, dim_head) and returns: attn, split_einsum