code: def load_model(
      keras,
      wrap_optimizer,
      filepath,
      custom_optimizers,
      custom_objects,
      legacy_opts=False,
  ):
      if legacy_opts:
          keras_subclasses = (
              keras.optimizers.legacy.Optimizer.__subclasses__()
          )
      else:
          keras_subclasses = keras.optimizers.Optimizer.__subclasses__()
  
      horovod_objects = {
          subclass.__name__.lower(): wrap_optimizer(subclass)
          for subclass in keras_subclasses
      }
  
      if custom_optimizers is not None:
          horovod_objects.update(
              {
                  cls.__name__: wrap_optimizer(cls)
                  for cls in custom_optimizers
              }
          )
  
      if custom_objects is not None:
          horovod_objects.update(custom_objects)
  
      return keras.models.load_model(
          filepath, custom_objects=horovod_objects
      )
  
file_path: horovod/horovod/_keras/__init__.py
imports: import os
  from packaging import version
  import horovod.tensorflow
  import tensorflow
  from horovod.tensorflow.gradient_aggregation import LocalGradientAggregationHelper
  from horovod.tensorflow.gradient_aggregation_eager import LocalGradientAggregationHelperEager
  from horovod.tensorflow.mpi_ops import rank, size_op
label_desc: Use image node_id 7 for calling a global function with example usage: load_model(keras, wrap_optimizer, filepath, custom_optimizers, custom_objects, legacy_opts) and returns: keras