code: def _preprocess_backward(
      out, do, l, block_q: int, debug: bool, interpret: bool
  ):
      batch_size, seq_len, num_heads, head_dim = out.shape
      out_shape = [
          jax.ShapeDtypeStruct(do.shape, do.dtype),
          jax.ShapeDtypeStruct(l.shape, l.dtype),
      ]
      do_scaled, delta = pl.pallas_call(
          functools.partial(
              _preprocess_backward_kernel, block_q=block_q
          ),
          grid=(pl.cdiv(seq_len, block_q), batch_size, num_heads),
          in_specs=[
              pl.BlockSpec(
                  lambda _, j, k: (j, 0, k, 0),
                  (None, seq_len, None, head_dim),
              ),
              pl.BlockSpec(
                  lambda _, j, k: (j, 0, k, 0),
                  (None, seq_len, None, head_dim),
              ),
              pl.BlockSpec(
                  lambda _, j, k: (j, k, 0), (None, None, seq_len)
              ),
          ],
          out_specs=[
              pl.BlockSpec(
                  lambda _, j, k: (j, 0, k, 0),
                  (None, seq_len, None, head_dim),
              ),
              pl.BlockSpec(
                  lambda _, j, k: (j, k, 0), (None, None, seq_len)
              ),
          ],
          num_warps=4,
          num_stages=3,
          out_shape=out_shape,
          debug=debug,
          interpret=interpret,
          name="mha_preprocess_backward",
      )(out, do, l)
      return do_scaled, delta
  
file_path: jax/jax/experimental/pallas/ops/attention.py
imports: from __future__ import annotations
  import functools
  from typing import Any, Optional
  import jax
  from jax import lax
  from jax.experimental import pallas
  import jax.numpy
  import numpy
label_desc: Use image node_id 6 for calling a global function with example usage: _preprocess_backward(out, do, l, block_q, debug, interpret) and returns: do_scaled,  delta