code: def _make_execution_function(model, mode):
      """Makes function to run one step of model execution."""
      if model._distribution_strategy:
          return distributed_training_utils_v1._make_execution_function(
              model, mode
          )
      return model._make_execution_function(mode)
  
imports: import functools
  import numpy
  from tensorflow.python.data.ops import iterator_ops
  from tensorflow.python.eager import context
  from tensorflow.python.framework import errors
  from tensorflow.python.keras import backend
  from tensorflow.python.keras import callbacks
  from tensorflow.python.keras.distribute import distributed_training_utils_v1
  from tensorflow.python.keras.engine import training_utils_v1
  from tensorflow.python.keras.utils.generic_utils import make_batches
  from tensorflow.python.keras.utils.generic_utils import slice_arrays
  from tensorflow.python.keras.utils.mode_keys import ModeKeys
  from tensorflow.python.platform import tf_logging
  from tensorflow.python.types import data
  from tensorflow.python.util import nest
label_desc: Use image node_id 8 for calling a global function with example usage: _make_execution_function(model, mode) and returns: model, distributed_training_utils_v1