code: def _conv_general_dilated_transpose_rhs(
      g,
      lhs,
      rhs,
      *,
      window_strides,
      padding,
      lhs_dilation,
      rhs_dilation,
      dimension_numbers: ConvDimensionNumbers,
      feature_group_count: int,
      batch_group_count: int,
      precision,
      preferred_element_type,
  ):
      assert type(dimension_numbers) is ConvDimensionNumbers
      if np.size(g) == 0:
          # Avoids forming degenerate convolutions where the RHS has spatial size 0.
          return ad.Zero(rhs.aval)
      lhs_shape = lhs.shape
      rhs_shape = rhs.aval.shape
      lhs_sdims, rhs_sdims, out_sdims = map(
          _conv_sdims, dimension_numbers
      )
      lhs_trans, rhs_trans, out_trans = map(
          _conv_spec_transpose, dimension_numbers
      )
      assert batch_group_count == 1 or feature_group_count == 1
      if batch_group_count > 1:
          feature_group_count = batch_group_count
          batch_group_count = 1
      elif feature_group_count > 1:
          batch_group_count = feature_group_count
          feature_group_count = 1
      trans_dimension_numbers = ConvDimensionNumbers(
          lhs_trans, out_trans, rhs_trans
      )
      padding = _conv_general_vjp_rhs_padding(
          np.take(lhs_shape, lhs_sdims),
          np.take(rhs_shape, rhs_sdims),
          window_strides,
          np.take(g.shape, out_sdims),
          padding,
          lhs_dilation,
          rhs_dilation,
      )
      return conv_general_dilated(
          lhs,
          g,
          window_strides=rhs_dilation,
          padding=padding,
          lhs_dilation=lhs_dilation,
          rhs_dilation=window_strides,
          dimension_numbers=trans_dimension_numbers,
          feature_group_count=feature_group_count,
          batch_group_count=batch_group_count,
          precision=precision,
          preferred_element_type=preferred_element_type,
      )
  
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 10 for calling a global function with example usage: _conv_general_dilated_transpose_rhs(g, lhs, rhs) and returns: conv_general_dilated, ad