code: def _conv_general_dilated_batch_rule(
      batched_args,
      batch_dims,
      *,
      window_strides,
      padding,
      lhs_dilation,
      rhs_dilation,
      dimension_numbers,
      feature_group_count,
      batch_group_count,
      precision,
      preferred_element_type,
      **unused_kwargs,
  ):
      assert batch_group_count == 1 or feature_group_count == 1
      lhs, rhs = batched_args
      lhs_bdim, rhs_bdim = batch_dims
      lhs_spec, rhs_spec, out_spec = dimension_numbers
  
      # Some of the cases that reshape into batch or feature dimensions do not work
      # with size 0 batch dimensions. The best fix would be to extend HLO to support
      # multiple batch dimensions.
      if (lhs_bdim is not None and lhs.shape[lhs_bdim] == 0) or (
          rhs_bdim is not None and rhs.shape[rhs_bdim] == 0
      ):
          lhs_shape_unbatched, rhs_shape_unbatched = list(
              lhs.shape
          ), list(rhs.shape)
          if lhs_bdim is not None:
              lhs_shape_unbatched.pop(lhs_bdim)
          if rhs_bdim is not None:
              rhs_shape_unbatched.pop(rhs_bdim)
          shape = _conv_general_dilated_shape_rule(
              core.ShapedArray(lhs_shape_unbatched, lhs.dtype),
              core.ShapedArray(rhs_shape_unbatched, rhs.dtype),
              window_strides=window_strides,
              padding=padding,
              lhs_dilation=lhs_dilation,
              rhs_dilation=rhs_dilation,
              dimension_numbers=dimension_numbers,
              feature_group_count=feature_group_count,
              batch_group_count=batch_group_count,
          )
          return (
              lax.full(
                  (0,) + shape,
                  0,
                  dtype=lhs.dtype
                  if preferred_element_type is None
                  else preferred_element_type,
              ),
              0,
          )
  
      if lhs_bdim is not None and rhs_bdim is not None:
          assert lhs.shape[lhs_bdim] == rhs.shape[rhs_bdim]
          if batch_group_count > 1:
              new_lhs = _reshape_axis_into(lhs_bdim, lhs_spec[0], lhs)
              batch_group_count *= lhs.shape[lhs_bdim]
          else:
              new_lhs = _reshape_axis_into(lhs_bdim, lhs_spec[1], lhs)
              feature_group_count *= lhs.shape[lhs_bdim]
          new_rhs = _reshape_axis_into(rhs_bdim, rhs_spec[0], rhs)
          out = conv_general_dilated(
              new_lhs,
              new_rhs,
              window_strides,
              padding,
              lhs_dilation,
              rhs_dilation,
              dimension_numbers,
              feature_group_count=feature_group_count,
              batch_group_count=batch_group_count,
              precision=precision,
              preferred_element_type=preferred_element_type,
          )
          out = _reshape_axis_out_of(
              out_spec[1], lhs.shape[lhs_bdim], out
          )
          return out, out_spec[1]
  
      elif lhs_bdim is not None:
          if batch_group_count == 1:
              new_lhs = _reshape_axis_into(lhs_bdim, lhs_spec[0], lhs)
              out = conv_general_dilated(
                  new_lhs,
                  rhs,
                  window_strides,
                  padding,
                  lhs_dilation,
                  rhs_dilation,
                  dimension_numbers,
                  feature_group_count,
                  precision=precision,
                  preferred_element_type=preferred_element_type,
              )
              out = _reshape_axis_out_of(
                  out_spec[0], lhs.shape[lhs_bdim], out
              )
              return out, out_spec[0]
          else:
              new_lhs = _reshape_axis_out_of(
                  lhs_spec[0] + int(lhs_bdim <= lhs_spec[0]),
                  batch_group_count,
                  lhs,
              )
              new_lhs = _reshape_axis_into(
                  lhs_bdim + int(lhs_spec[0] < lhs_bdim),
                  lhs_spec[0] + 1,
                  new_lhs,
              )
              new_lhs = _reshape_axis_into(
                  lhs_spec[0], lhs_spec[0], new_lhs
              )
              out = conv_general_dilated(
                  new_lhs,
                  rhs,
                  window_strides,
                  padding,
                  lhs_dilation,
                  rhs_dilation,
                  dimension_numbers,
                  feature_group_count,
                  batch_group_count,
                  precision=precision,
                  preferred_element_type=preferred_element_type,
              )
              out = _reshape_axis_out_of(
                  out_spec[0], lhs.shape[lhs_bdim], out
              )
              return out, out_spec[0]
  
      elif rhs_bdim is not None:
          if feature_group_count == 1 and batch_group_count == 1:
              new_rhs = _reshape_axis_into(rhs_bdim, rhs_spec[0], rhs)
              out = conv_general_dilated(
                  lhs,
                  new_rhs,
                  window_strides,
                  padding,
                  lhs_dilation,
                  rhs_dilation,
                  dimension_numbers,
                  feature_group_count,
                  batch_group_count,
                  precision=precision,
                  preferred_element_type=preferred_element_type,
              )
              out = _reshape_axis_out_of(
                  out_spec[1], rhs.shape[rhs_bdim], out
              )
              return out, out_spec[1]
          else:
              # groups need to be outermost, so we need to factor them out of the
              # rhs output feature dim, then factor the batch dim into the remaining rhs
              # output feature dim, then put groups back in. We do something
              # similar on the output. An alternative which would require more FLOPs but
              # fewer reshapes would be to broadcast lhs.
              group_count = (
                  feature_group_count
                  if feature_group_count > 1
                  else batch_group_count
              )
              new_rhs = _reshape_axis_out_of(
                  rhs_spec[0] + int(rhs_bdim <= rhs_spec[0]),
                  group_count,
                  rhs,
              )
              new_rhs = _reshape_axis_into(
                  rhs_bdim + int(rhs_spec[0] < rhs_bdim),
                  rhs_spec[0] + 1,
                  new_rhs,
              )
              new_rhs = _reshape_axis_into(
                  rhs_spec[0], rhs_spec[0], new_rhs
              )
              out = conv_general_dilated(
                  lhs,
                  new_rhs,
                  window_strides,
                  padding,
                  lhs_dilation,
                  rhs_dilation,
                  dimension_numbers,
                  feature_group_count,
                  batch_group_count,
                  precision=precision,
                  preferred_element_type=preferred_element_type,
              )
              out = _reshape_axis_out_of(out_spec[1], group_count, out)
              out = _reshape_axis_out_of(
                  out_spec[1] + 1, rhs.shape[rhs_bdim], out
              )
              out = _reshape_axis_into(
                  out_spec[1], out_spec[1] + 1, out
              )
              return out, out_spec[1]
  
imports: from __future__ import annotations
  from collections.abc import Sequence
  from functools import partial
  import operator
  from typing import NamedTuple, Union
  import numpy
  from jax._src import core
  from jax._src import dtypes
  from jax._src import util
  from jax._src.interpreters import ad
  from jax._src.interpreters import batching
  from jax._src.interpreters import mlir
  from jax._src.lax import lax
  from jax._src.lib.mlir.dialects import hlo
  from jax._src.typing import Array, DTypeLike
label_desc: Use image node_id 11 for calling a global function with example usage: _conv_general_dilated_batch_rule(batched_args, batch_dims) and returns: lax,  int, out,  out_spec, out,  out_spec, out,  out_spec, out,  out_spec, out,  out_spec