How to use the tensorflowjs.converters.tf_saved_model_conversion_v2 function in tensorflowjs

def export_tfjs_model(yolo, path):
    import tensorflowjs as tfjs
    import tempfile
    overwrite_path(path)

    temp_savedmodel_dir = tempfile.mktemp(suffix='.savedmodel')
    tf.keras.experimental.export_saved_model(
        yolo.yolo_model, temp_savedmodel_dir, serving_only=True)

    tfjs.converters.tf_saved_model_conversion_v2.convert_tf_saved_model(
        temp_savedmodel_dir,
        path,
        signature_def='serving_default',
        saved_model_tags='serve')
    # tfjs.converters.save_keras_model(yolo.yolo_model,

if not os.path.exists(h5_path):
    raise ValueError('Nonexistent path to HDF5 file: %s' % h5_path)
  if os.path.isdir(h5_path):
    raise ValueError(
        'Expected path to point to an HDF5 file, but it points to a '
        'directory: %s' % h5_path)

  temp_savedmodel_dir = tempfile.mktemp(suffix='.savedmodel')
  model = keras.models.load_model(h5_path)
  keras.experimental.export_saved_model(
      model, temp_savedmodel_dir, serving_only=True)

  # NOTE(cais): This cannot use `tf.compat.v1` because
  #   `convert_tf_saved_model()` works only in v2.
  tf_saved_model_conversion_v2.convert_tf_saved_model(
      temp_savedmodel_dir, output_dir,
      signature_def='serving_default',
      saved_model_tags='serve',
      quantization_dtype=quantization_dtype,
      skip_op_check=skip_op_check,
      strip_debug_ops=strip_debug_ops)

  # Clean up the temporary SavedModel directory.
  shutil.rmtree(temp_savedmodel_dir)

if not os.path.exists(h5_path):
    raise ValueError('Nonexistent path to HDF5 file: %s' % h5_path)
  if os.path.isdir(h5_path):
    raise ValueError(
        'Expected path to point to an HDF5 file, but it points to a '
        'directory: %s' % h5_path)

  temp_savedmodel_dir = tempfile.mktemp(suffix='.savedmodel')
  model = keras.models.load_model(h5_path)
  keras.experimental.export_saved_model(
      model, temp_savedmodel_dir, serving_only=True)

  # NOTE(cais): This cannot use `tf.compat.v1` because
  #   `convert_tf_saved_model()` works only in v2.
  tf_saved_model_conversion_v2.convert_tf_saved_model(
      temp_savedmodel_dir, output_dir,
      signature_def='serving_default',
      saved_model_tags='serve',
      quantization_dtype=quantization_dtype,
      skip_op_check=skip_op_check,
      strip_debug_ops=strip_debug_ops)

  # Clean up the temporary SavedModel directory.
  shutil.rmtree(temp_savedmodel_dir)

elif (input_format == common.TFJS_LAYERS_MODEL and
        output_format == common.TFJS_LAYERS_MODEL):
    dispatch_tensorflowjs_to_tensorflowjs_conversion(
        args.input_path, args.output_path,
        quantization_dtype=_parse_quantization_bytes(args.quantization_bytes),
        weight_shard_size_bytes=weight_shard_size_bytes)
  elif (input_format == common.TFJS_LAYERS_MODEL and
        output_format == common.TFJS_GRAPH_MODEL):
    dispatch_tfjs_layers_model_to_tfjs_graph_conversion(
        args.input_path, args.output_path,
        quantization_dtype=_parse_quantization_bytes(args.quantization_bytes),
        skip_op_check=args.skip_op_check,
        strip_debug_ops=args.strip_debug_ops)
  elif (input_format == common.TF_FROZEN_MODEL and
        output_format == common.TFJS_GRAPH_MODEL):
    tf_saved_model_conversion_v2.convert_tf_frozen_model(
        args.input_path, args.output_node_names, args.output_path,
        quantization_dtype=_parse_quantization_bytes(args.quantization_bytes),
        skip_op_check=args.skip_op_check,
        strip_debug_ops=args.strip_debug_ops)
  else:
    raise ValueError(
        'Unsupported input_format - output_format pair: %s - %s' %
        (input_format, output_format))

dispatch_keras_saved_model_to_tensorflowjs_conversion(
        args.input_path, args.output_path,
        quantization_dtype=quantization_dtype,
        split_weights_by_layer=args.split_weights_by_layer)
  elif (input_format == common.TF_SAVED_MODEL and
        output_format == common.TFJS_GRAPH_MODEL):
    tf_saved_model_conversion_v2.convert_tf_saved_model(
        args.input_path, args.output_path,
        signature_def=args.signature_name,
        saved_model_tags=args.saved_model_tags,
        quantization_dtype=quantization_dtype,
        skip_op_check=args.skip_op_check,
        strip_debug_ops=args.strip_debug_ops)
  elif (input_format == common.TF_HUB_MODEL and
        output_format == common.TFJS_GRAPH_MODEL):
    tf_saved_model_conversion_v2.convert_tf_hub_module(
        args.input_path, args.output_path, args.signature_name,
        args.saved_model_tags, skip_op_check=args.skip_op_check,
        strip_debug_ops=args.strip_debug_ops)
  elif (input_format == common.TFJS_LAYERS_MODEL and
        output_format == common.KERAS_MODEL):
    dispatch_tensorflowjs_to_keras_h5_conversion(args.input_path,
                                                 args.output_path)
  elif (input_format == common.TFJS_LAYERS_MODEL and
        output_format == common.KERAS_SAVED_MODEL):
    dispatch_tensorflowjs_to_keras_saved_model_conversion(args.input_path,
                                                          args.output_path)
  elif (input_format == common.TFJS_LAYERS_MODEL and
        output_format == common.TFJS_LAYERS_MODEL):
    dispatch_tensorflowjs_to_tensorflowjs_conversion(
        args.input_path, args.output_path,
        quantization_dtype=_parse_quantization_bytes(args.quantization_bytes),

dispatch_keras_saved_model_to_tensorflowjs_conversion(
        args.input_path, args.output_path,
        quantization_dtype=quantization_dtype,
        split_weights_by_layer=args.split_weights_by_layer)
  elif (input_format == common.TF_SAVED_MODEL and
        output_format == common.TFJS_GRAPH_MODEL):
    tf_saved_model_conversion_v2.convert_tf_saved_model(
        args.input_path, args.output_path,
        signature_def=args.signature_name,
        saved_model_tags=args.saved_model_tags,
        quantization_dtype=quantization_dtype,
        skip_op_check=args.skip_op_check,
        strip_debug_ops=args.strip_debug_ops)
  elif (input_format == common.TF_HUB_MODEL and
        output_format == common.TFJS_GRAPH_MODEL):
    tf_saved_model_conversion_v2.convert_tf_hub_module(
        args.input_path, args.output_path, args.signature_name,
        args.saved_model_tags, skip_op_check=args.skip_op_check,
        strip_debug_ops=args.strip_debug_ops)
  elif (input_format == common.TFJS_LAYERS_MODEL and
        output_format == common.KERAS_MODEL):
    dispatch_tensorflowjs_to_keras_h5_conversion(args.input_path,
                                                 args.output_path)
  elif (input_format == common.TFJS_LAYERS_MODEL and
        output_format == common.KERAS_SAVED_MODEL):
    dispatch_tensorflowjs_to_keras_saved_model_conversion(args.input_path,
                                                          args.output_path)
  elif (input_format == common.TFJS_LAYERS_MODEL and
        output_format == common.TFJS_LAYERS_MODEL):
    dispatch_tensorflowjs_to_tensorflowjs_conversion(
        args.input_path, args.output_path,
        quantization_dtype=_parse_quantization_bytes(args.quantization_bytes),