code: def _conv_general_dilated_transpose_lhs(
      g,
      lhs,
      rhs,
      *,
      window_strides,
      padding,
      lhs_dilation,
      rhs_dilation,
      dimension_numbers,
      feature_group_count,
      batch_group_count,
      precision,
      preferred_element_type,
  ):
      assert type(dimension_numbers) is ConvDimensionNumbers
      assert batch_group_count == 1 or feature_group_count == 1
      rhs_shape = rhs.shape
      lhs_shape = lhs.aval.shape
      lhs_sdims, rhs_sdims, out_sdims = map(
          _conv_sdims, dimension_numbers
      )
      lhs_spec, rhs_spec, out_spec = dimension_numbers
      t_rhs_spec = _conv_spec_transpose(rhs_spec)
      if feature_group_count > 1:
          # in addition to switching the dims in the spec, need to move the feature
          # group axis into the transposed rhs's output feature dim
          rhs = _reshape_axis_out_of(
              rhs_spec[0], feature_group_count, rhs
          )
          rhs = _reshape_axis_into(rhs_spec[0], rhs_spec[1], rhs)
      elif batch_group_count > 1:
          rhs = _reshape_axis_out_of(
              rhs_spec[0], batch_group_count, rhs
          )
          rhs = _reshape_axis_into(rhs_spec[0], rhs_spec[1], rhs)
          feature_group_count = batch_group_count
      trans_dimension_numbers = ConvDimensionNumbers(
          out_spec, t_rhs_spec, lhs_spec
      )
      padding = _conv_general_vjp_lhs_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,
      )
      revd_weights = lax.rev(rhs, rhs_sdims)
      out = conv_general_dilated(
          g,
          revd_weights,
          window_strides=lhs_dilation,
          padding=padding,
          lhs_dilation=window_strides,
          rhs_dilation=rhs_dilation,
          dimension_numbers=trans_dimension_numbers,
          feature_group_count=feature_group_count,
          batch_group_count=1,
          precision=precision,
          preferred_element_type=preferred_element_type,
      )
      if batch_group_count > 1:
          out = _reshape_axis_out_of(
              lhs_spec[1], batch_group_count, out
          )
          out = _reshape_axis_into(lhs_spec[1], lhs_spec[0], out)
      return out
  
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 9 for calling a global function with example usage: _conv_general_dilated_transpose_lhs(g, lhs, rhs) and returns: out