code: def conv_dimension_numbers(
      lhs_shape, rhs_shape, dimension_numbers
  ) -> ConvDimensionNumbers:
      """Converts convolution `dimension_numbers` to a `ConvDimensionNumbers`.
  
      Args:
        lhs_shape: tuple of nonnegative integers, shape of the convolution input.
        rhs_shape: tuple of nonnegative integers, shape of the convolution kernel.
        dimension_numbers: None or a tuple/list of strings or a ConvDimensionNumbers
          object following the convolution dimension number specification format in
          xla_client.py.
  
      Returns:
        A `ConvDimensionNumbers` object that represents `dimension_numbers` in the
        canonical form used by lax functions.
      """
      if isinstance(dimension_numbers, ConvDimensionNumbers):
          return dimension_numbers
      if len(lhs_shape) != len(rhs_shape):
          msg = "convolution requires lhs and rhs ndim to be equal, got {} and {}."
          raise TypeError(msg.format(len(lhs_shape), len(rhs_shape)))
  
      if dimension_numbers is None:
          iota = tuple(range(len(lhs_shape)))
          return ConvDimensionNumbers(iota, iota, iota)
      elif isinstance(dimension_numbers, (list, tuple)):
          if len(dimension_numbers) != 3:
              msg = "convolution dimension_numbers list/tuple must be length 3, got {}."
              raise TypeError(msg.format(len(dimension_numbers)))
          if not all(isinstance(elt, str) for elt in dimension_numbers):
              msg = "convolution dimension_numbers elements must be strings, got {}."
              raise TypeError(
                  msg.format(tuple(map(type, dimension_numbers)))
              )
          msg = (
              "convolution dimension_numbers[{}] must have len equal to the ndim "
              "of lhs and rhs, got {} for lhs and rhs shapes {} and {}."
          )
          for i, elt in enumerate(dimension_numbers):
              if len(elt) != len(lhs_shape):
                  raise TypeError(
                      msg.format(i, len(elt), lhs_shape, rhs_shape)
                  )
  
          lhs_spec, rhs_spec, out_spec = conv_general_permutations(
              dimension_numbers
          )
          return ConvDimensionNumbers(lhs_spec, rhs_spec, out_spec)
      else:
          msg = "convolution dimension_numbers must be tuple/list or None, got {}."
          raise TypeError(msg.format(type(dimension_numbers)))
  
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 19 for calling a global function with example usage: conv_dimension_numbers(lhs_shape, rhs_shape, dimension_numbers) and returns: dimension_numbers, ConvDimensionNumbers, ConvDimensionNumbers