code: def load_model(
      filepath,
      custom_optimizers=None,
      custom_objects=None,
      compression=Compression.none,
  ):
      """
      Loads a saved Keras model with a BytePS DistributedOptimizer.
      The DistributedOptimizer will wrap the underlying optimizer used to train
      the saved model, so that the optimizer state (params and weights) will
      be picked up for retraining.
      By default, all optimizers in the module `keras.optimizers` will be loaded
      and wrapped without needing to specify any `custom_optimizers` or
      `custom_objects`.
      # Arguments
          filepath: One of the following:
              - string, path to the saved model, or
              - h5py.File object from which to load the model
          custom_optimizers: Optional list of Optimizer subclasses to support
              during loading.
          custom_objects: Optional dictionary mapping names (strings) to custom
              classes or functions to be considered during deserialization.
          compression: Compression algorithm used to reduce the amount of data
                       sent and received by each worker node.  Defaults to not
                       using compression.
      # Returns
          A Keras model instance.
      # Raises
          ImportError: If h5py is not available.
          ValueError: In case of an invalid savefile.
      """
  
      def wrap_optimizer(cls):
          return lambda **kwargs: DistributedOptimizer(
              cls(**kwargs), compression=compression
          )
  
      optimizer_modules = {keras.optimizers.Optimizer.__module__}
      return _impl.load_model(
          keras,
          wrap_optimizer,
          optimizer_modules,
          filepath,
          custom_optimizers,
          custom_objects,
      )
  
file_path: byteps/byteps/keras/__init__.py
imports: import keras
  import keras.backend
  from byteps.tensorflow import init
  from byteps.tensorflow import shutdown
  from byteps.tensorflow import size
  from byteps.tensorflow import local_size
  from byteps.tensorflow import rank
  from byteps.tensorflow import local_rank
  from byteps.tensorflow import Compression
  from byteps.keras import callbacks
  import byteps._keras
label_desc: Use image node_id 5 for calling a global function with example usage: load_model(filepath, custom_optimizers, custom_objects, compression) and returns: _impl, function