code: def export(self, path, _sentinel=None,  # pylint: disable=invalid-name
               checkpoint_path=None, name_transform_fn=None):
      
      from tensorflow_hub.module import export_module_spec  # pylint: disable=g-import-not-at-top
      if not checkpoint_path:
        raise ValueError("Missing mandatory `checkpoint_path` parameter")
      name_transform_fn = name_transform_fn or (lambda x: x)
      export_module_spec(self, path, checkpoint_path, name_transform_fn)
docs: Exports a ModuleSpec with weights taken from a checkpoint.
  
      This is an helper to export modules directly from a ModuleSpec
      without having to create a session and set the variables to the
      intended values.
  
      Example usage:
  
      ```python
      spec = hub.create_module_spec(module_fn)
      spec.export("/path/to/export_module",
                  checkpoint_path="/path/to/training_model")
      ```
  
      In some cases, the variable name in the checkpoint does not match
      the variable name in the module. It is possible to work around that
      by providing a checkpoint_map_fn that performs the variable mapping.
      For example with: `name_transform_fn = lambda x: "extra_scope/" + x`.
  
      Args:
        path: path where to export the module to.
        _sentinel: used to prevent positional arguments besides `path`.
        checkpoint_path: path where to load the weights for the module.
          Mandatory parameter and must be passed by name.
        name_transform_fn: optional function to provide mapping between
          variable name in the module and the variable name in the checkpoint.
  
      Raises:
        ValueError: if missing mandatory `checkpoint_path` parameter.