code: def _conv_general_dilated_shape_rule(
      lhs: core.ShapedArray,
      rhs: core.ShapedArray,
      *,
      window_strides,
      padding,
      lhs_dilation,
      rhs_dilation,
      dimension_numbers,
      feature_group_count,
      batch_group_count,
      **unused_kwargs,
  ) -> tuple[int, ...]:
      assert type(dimension_numbers) is ConvDimensionNumbers
      if len(lhs.shape) != len(rhs.shape):
          msg = (
              "conv_general_dilated lhs and rhs must have the same number of "
              "dimensions, but got {} and {}."
          )
          raise ValueError(msg.format(lhs.shape, rhs.shape))
      if not feature_group_count > 0:
          msg = (
              "conv_general_dilated feature_group_count "
              "must be a positive integer, got {}."
          )
          raise ValueError(msg.format(feature_group_count))
      lhs_feature_count = lhs.shape[dimension_numbers.lhs_spec[1]]
      quot, rem = divmod(lhs_feature_count, feature_group_count)
      if rem:
          msg = (
              "conv_general_dilated feature_group_count must divide lhs feature "
              "dimension size, but {} does not divide {}."
          )
          raise ValueError(
              msg.format(feature_group_count, lhs_feature_count)
          )
      if not core.definitely_equal(
          quot, rhs.shape[dimension_numbers.rhs_spec[1]]
      ):
          msg = (
              "conv_general_dilated lhs feature dimension size divided by "
              "feature_group_count must equal the rhs input feature dimension "
              "size, but {} // {} != {}."
          )
          raise ValueError(
              msg.format(
                  lhs_feature_count,
                  feature_group_count,
                  rhs.shape[dimension_numbers.rhs_spec[1]],
              )
          )
      if rhs.shape[dimension_numbers.rhs_spec[0]] % feature_group_count:
          msg = (
              "conv_general_dilated rhs output feature dimension size must be a "
              "multiple of feature_group_count, but {} is not a multiple of {}."
          )
          raise ValueError(
              msg.format(
                  rhs.shape[dimension_numbers.rhs_spec[0]],
                  feature_group_count,
              )
          )
  
      if not batch_group_count > 0:
          msg = (
              "conv_general_dilated batch_group_count "
              "must be a positive integer, got {}."
          )
          raise ValueError(msg.format(batch_group_count))
      lhs_batch_count = lhs.shape[dimension_numbers.lhs_spec[0]]
      if (
          batch_group_count > 1
          and lhs_batch_count % batch_group_count != 0
      ):
          msg = (
              "conv_general_dilated batch_group_count must divide lhs batch "
              "dimension size, but {} does not divide {}."
          )
          raise ValueError(
              msg.format(batch_group_count, lhs_batch_count)
          )
  
      if rhs.shape[dimension_numbers.rhs_spec[0]] % batch_group_count:
          msg = (
              "conv_general_dilated rhs output feature dimension size must be a "
              "multiple of batch_group_count, but {} is not a multiple of {}."
          )
          raise ValueError(
              msg.format(
                  rhs.shape[dimension_numbers.rhs_spec[0]],
                  batch_group_count,
              )
          )
  
      if batch_group_count > 1 and feature_group_count > 1:
          msg = (
              "At most one of batch_group_count and feature_group_count may be > "
              "1, got batch_group_count={} and feature_group_count={}"
          )
          raise ValueError(
              msg.format(batch_group_count, feature_group_count)
          )
  
      if len(_conv_sdims(dimension_numbers.rhs_spec)) != len(
          window_strides
      ):
          msg = (
              "conv_general_dilated window and window_strides must have "
              "the same number of dimensions, but got {} and {}"
          )
          raise ValueError(
              msg.format(
                  len(_conv_sdims(dimension_numbers.rhs_spec)),
                  len(window_strides),
              )
          )
  
      lhs_perm, rhs_perm, out_perm = dimension_numbers
      lhs_trans = lax._dilate_shape(
          np.take(lhs.shape, lhs_perm), lhs_dilation
      )
      rhs_trans = lax._dilate_shape(
          np.take(rhs.shape, rhs_perm), rhs_dilation
      )
      out_trans = conv_shape_tuple(
          lhs_trans,
          rhs_trans,
          window_strides,
          padding,
          batch_group_count,
      )
      return tuple(np.take(out_trans, np.argsort(out_perm)))
  
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 7 for calling a global function with example usage: _conv_general_dilated_shape_rule(lhs, rhs) and returns: tuple