code: def split_einsum(q, k, v, mask, heads, dim_head):
      """Attention Implementation backing AttentionImplementations.SPLIT_EINSUM
  
      - Implements https://machinelearning.apple.com/research/neural-engine-transformers
      - Recommended for ANE
      - Marginally slower on GPU
      """
      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
  
      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, ki]) * (dim_head**-0.5)
          for qi, ki in zip(mh_q, mh_k)
      ]  # (bs, max_seq_length, 1, max_seq_length) * heads
  
      if mask is not None:
          for head_idx in range(heads):
              attn_weights[head_idx] = attn_weights[head_idx] + mask
  
      attn_weights = [
          aw.softmax(dim=1) for aw in attn_weights
      ]  # (bs, max_seq_length, 1, max_seq_length) * heads
      attn = [
          torch.einsum("bkhq,bchk->bchq", wi, vi)
          for wi, vi in zip(attn_weights, mh_v)
      ]  # (bs, dim_head, 1, max_seq_length) * heads
  
      attn = torch.cat(attn, dim=1)  # (bs, dim, 1, max_seq_length)
      return attn
  
imports: import logging
  import torch
label_desc: Use image node_id 1 for calling a global function with example usage: split_einsum(q, k, v, mask, heads, dim_head) and returns: attn