How to use the tensorflowjs.converters.keras_tfjs_loader.load_keras_model function in tensorflowjs

'For input_type=tfjs_layers_model & output_format=keras, '
        'the output path should be the path to an HDF5 file, '
        'but received an existing directory (%s).' % h5_path)

  # Verify that config_json_path points to a JSON file.
  with open(config_json_path, 'rt') as f:
    try:
      json.load(f)
    except (ValueError, IOError):
      raise ValueError(
          'For input_type=tfjs_layers_model & output_format=keras, '
          'the input path is expected to contain valid JSON content, '
          'but cannot read valid JSON content from %s.' % config_json_path)

  with tf.Graph().as_default(), tf.compat.v1.Session():
    model = keras_tfjs_loader.load_keras_model(config_json_path)
    model.save(h5_path)

# TODO(cais): Assert output_dir_path doesn't exist or is the path to
  # a directory (not a file).

  # Verify that config_json_path points to a JSON file.
  with open(config_json_path, 'rt') as f:
    try:
      json.load(f)
    except (ValueError, IOError):
      raise ValueError(
          'For input_type=tfjs_layers_model, '
          'the input path is expected to contain valid JSON content, '
          'but cannot read valid JSON content from %s.' % config_json_path)

  temp_h5_path = tempfile.mktemp(suffix='.h5')

  model = keras_tfjs_loader.load_keras_model(config_json_path)
  model.save(temp_h5_path)
  dispatch_keras_h5_to_tfjs_graph_model_conversion(
      temp_h5_path, output_dir_path,
      quantization_dtype=quantization_dtype,
      skip_op_check=skip_op_check,
      strip_debug_ops=strip_debug_ops)

  # Clean up temporary HDF5 file.
  os.remove(temp_h5_path)

'For input_type=tfjs_layers_model & output_format=keras_saved_model, '
        'the input path should be a model.json '
        'file, but received a directory.')

  # Verify that config_json_path points to a JSON file.
  with open(config_json_path, 'rt') as f:
    try:
      json.load(f)
    except (ValueError, IOError):
      raise ValueError(
          'For input_type=tfjs_layers_model & output_format=keras, '
          'the input path is expected to contain valid JSON content, '
          'but cannot read valid JSON content from %s.' % config_json_path)

  with tf.Graph().as_default(), tf.compat.v1.Session():
    model = keras_tfjs_loader.load_keras_model(config_json_path)
    keras.experimental.export_saved_model(
        model, keras_saved_model_path, serving_only=True)

'For input_type=tfjs_layers_model & output_format=keras, '
        'the output path should be the path to an HDF5 file, '
        'but received an existing directory (%s).' % h5_path)

  # Verify that config_json_path points to a JSON file.
  with open(config_json_path, 'rt') as f:
    try:
      json.load(f)
    except (ValueError, IOError):
      raise ValueError(
          'For input_type=tfjs_layers_model & output_format=keras, '
          'the input path is expected to contain valid JSON content, '
          'but cannot read valid JSON content from %s.' % config_json_path)

  with tf.Graph().as_default(), tf.compat.v1.Session():
    model = keras_tfjs_loader.load_keras_model(config_json_path)
    model.save(h5_path)

'For input_type=tfjs_layers_model & output_format=keras_saved_model, '
        'the input path should be a model.json '
        'file, but received a directory.')

  # Verify that config_json_path points to a JSON file.
  with open(config_json_path, 'rt') as f:
    try:
      json.load(f)
    except (ValueError, IOError):
      raise ValueError(
          'For input_type=tfjs_layers_model & output_format=keras, '
          'the input path is expected to contain valid JSON content, '
          'but cannot read valid JSON content from %s.' % config_json_path)

  with tf.Graph().as_default(), tf.compat.v1.Session():
    model = keras_tfjs_loader.load_keras_model(config_json_path)
    keras.experimental.export_saved_model(
        model, keras_saved_model_path, serving_only=True)

# TODO(cais): Assert output_dir_path doesn't exist or is the path to
  # a directory (not a file).

  # Verify that config_json_path points to a JSON file.
  with open(config_json_path, 'rt') as f:
    try:
      json.load(f)
    except (ValueError, IOError):
      raise ValueError(
          'For input_type=tfjs_layers_model, '
          'the input path is expected to contain valid JSON content, '
          'but cannot read valid JSON content from %s.' % config_json_path)

  temp_h5_path = tempfile.mktemp(suffix='.h5')
  with tf.Graph().as_default(), tf.compat.v1.Session():
    model = keras_tfjs_loader.load_keras_model(config_json_path)
    model.save(temp_h5_path)
    dispatch_tensorflowjs_to_keras_h5_conversion(config_json_path, temp_h5_path)

  with tf.Graph().as_default(), tf.compat.v1.Session():
    dispatch_keras_h5_to_tfjs_layers_model_conversion(
        temp_h5_path, output_dir_path,
        quantization_dtype=quantization_dtype,
        weight_shard_size_bytes=weight_shard_size_bytes)
    # TODO(cais): Support weight quantization.

  # Clean up the temporary H5 file.
  os.remove(temp_h5_path)