code: def _convert_to_lax_conv_dimension_numbers(
      num_spatial_dims,
      data_format="channels_last",
      transpose=False,
  ):
      """Create a `lax.ConvDimensionNumbers` for the given inputs."""
      num_dims = num_spatial_dims + 2
  
      if data_format == "channels_last":
          spatial_dims = tuple(range(1, num_dims - 1))
          inputs_dn = (0, num_dims - 1) + spatial_dims
      else:
          spatial_dims = tuple(range(2, num_dims))
          inputs_dn = (0, 1) + spatial_dims
  
      if transpose:
          kernel_dn = (num_dims - 2, num_dims - 1) + tuple(
              range(num_dims - 2)
          )
      else:
          kernel_dn = (num_dims - 1, num_dims - 2) + tuple(
              range(num_dims - 2)
          )
  
      return lax.ConvDimensionNumbers(
          lhs_spec=inputs_dn, rhs_spec=kernel_dn, out_spec=inputs_dn
      )
  
imports: import jax
  import numpy
  from jax import lax
  from jax import numpy
  from keras.backend import standardize_data_format
  from keras.backend import standardize_dtype
  from keras.backend.common.backend_utils import compute_conv_transpose_padding_args_for_jax
  from keras.backend.config import epsilon
  from keras.backend.numpy.core import cast
  from keras.backend.numpy.core import convert_to_tensor
  from keras.backend.numpy.core import is_tensor
  from keras.utils.module_utils import scipy
label_desc: Use image node_id 21 for calling a global function with example usage: _convert_to_lax_conv_dimension_numbers(num_spatial_dims, data_format, transpose) and returns: lax