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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def export(self, path, _sentinel=None, # pylint: disable=invalid-name checkpoint_path=None, name_transform_fn=None):
 """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. """

    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)