code: def _conv_general_dilated_lower(
      ctx,
      lhs,
      rhs,
      *,
      window_strides,
      padding,
      lhs_dilation,
      rhs_dilation,
      dimension_numbers,
      feature_group_count,
      batch_group_count,
      precision,
      preferred_element_type,
      expand_complex_convolutions=False,
      **unused_kwargs,
  ):
      lhs_aval, rhs_aval = ctx.avals_in
      (aval_out,) = ctx.avals_out
      assert isinstance(dimension_numbers, ConvDimensionNumbers)
      dtype = lhs_aval.dtype
      if expand_complex_convolutions and np.issubdtype(
          dtype, np.complexfloating
      ):
          if preferred_element_type is not None:
              # Convert complex dtype to types used for real and imaginary parts
              assert np.issubdtype(
                  preferred_element_type, np.complexfloating
              )
              preferred_element_type = _real_dtype(
                  preferred_element_type
              )
          complex_conv = mlir.lower_fun(
              partial(
                  _complex_mul,
                  partial(
                      conv_general_dilated,
                      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,
                      precision=precision,
                      preferred_element_type=preferred_element_type,
                  ),
              ),
              multiple_results=False,
          )
          return complex_conv(ctx, lhs, rhs)
  
      lhs_spec, rhs_spec, out_spec = dimension_numbers
      dnums = hlo.ConvDimensionNumbers.get(
          input_batch_dimension=lhs_spec[0],
          input_feature_dimension=lhs_spec[1],
          input_spatial_dimensions=list(lhs_spec[2:]),
          kernel_output_feature_dimension=rhs_spec[0],
          kernel_input_feature_dimension=rhs_spec[1],
          kernel_spatial_dimensions=list(rhs_spec[2:]),
          output_batch_dimension=out_spec[0],
          output_feature_dimension=out_spec[1],
          output_spatial_dimensions=list(out_spec[2:]),
      )
      num_spatial_dims = len(rhs_spec) - 2
      if len(padding) == 0:
          padding = np.zeros((0, 2), dtype=np.int64)
      window_reversal = mlir.dense_bool_elements(
          [False] * num_spatial_dims
      )
      if (
          not core.is_constant_shape(window_strides)
          or not core.is_constant_shape(lhs_dilation)
          or not core.is_constant_shape(rhs_dilation)
          or not core.is_constant_dim(feature_group_count)
          or not core.is_constant_dim(batch_group_count)
      ):
          # TODO(https://github.com/openxla/stablehlo/issues/1268)
          raise NotImplementedError(
              "Convolutions with non-static strides, dilation, feature_group_count, or batch_group_count"
          )
      if all(core.is_constant_shape(p) for p in padding):
          return [
              hlo.convolution(
                  mlir.aval_to_ir_type(aval_out),
                  lhs,
                  rhs,
                  dimension_numbers=dnums,
                  feature_group_count=mlir.i64_attr(
                      feature_group_count
                  ),
                  batch_group_count=mlir.i64_attr(batch_group_count),
                  window_strides=mlir.dense_int_elements(
                      window_strides
                  ),
                  padding=mlir.dense_int_elements(padding),
                  lhs_dilation=mlir.dense_int_elements(lhs_dilation),
                  rhs_dilation=mlir.dense_int_elements(rhs_dilation),
                  window_reversal=window_reversal,
                  precision_config=lax.precision_attr(precision),
              )
          ]
      else:
          # d_padding will be an array i32[N, 2] with pad_lo and pad_hi for each
          # spatial dimension.
          int2d = mlir.aval_to_ir_type(
              core.ShapedArray((1, 2), np.int32)
          )
  
          def prep_one_pad(
              pad_lo_hi: tuple[core.DimSize, core.DimSize]
          ):
              pad1 = mlir.eval_dynamic_shape_as_tensor(
                  ctx, pad_lo_hi
              )  # i32[2]
              return hlo.ReshapeOp(int2d, pad1)
  
          d_padding = hlo.ConcatenateOp(
              list(map(prep_one_pad, padding)), mlir.i64_attr(0)
          )
          return [
              hlo.dynamic_conv(
                  mlir.aval_to_ir_type(aval_out),
                  lhs,
                  rhs,
                  d_padding,
                  dimension_numbers=dnums,
                  feature_group_count=mlir.i64_attr(
                      feature_group_count
                  ),
                  batch_group_count=mlir.i64_attr(batch_group_count),
                  window_strides=mlir.dense_int_elements(
                      window_strides
                  ),
                  lhs_dilation=mlir.dense_int_elements(lhs_dilation),
                  rhs_dilation=mlir.dense_int_elements(rhs_dilation),
                  window_reversal=window_reversal,
                  precision_config=lax.precision_attr(precision),
              )
          ]
  
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 13 for calling a global function with example usage: _conv_general_dilated_lower(ctx, lhs, rhs) and returns: complex_conv, list, list, hlo