Tensorflow經常看到定義的函式前面加了“@tf_export”。例如，tensorflow/python/platform/app.py中有：

@tf_export('app.run')
def run(main=None, argv=None):
  """Runs the program with an optional 'main' function and 'argv' list."""

  # Define help flags.
  _define_help_flags()

  # Parse known flags.
  argv = flags.FLAGS(_sys.argv if argv is None else argv, known_only=True)

  main = main or _sys.modules['__main__'].main

  # Call the main function, passing through any arguments
  # to the final program.
  _sys.exit(main(argv))
 

首先，@tf_export是一個修飾符。修飾符的本質是一個函式，不懂可以撮戳這裡。

tf_export的實現在tensorflow/python/util/tf_export.py中：

tf_export = functools.partial(api_export, api_name=TENSORFLOW_API_NAME)

  等號的右邊的理解分兩步：

  1.functools.partial

  2.ap_export

   functools.partial是偏函式,它的本質簡而言之是為函式固定某些引數。如：functools.partial(FuncA, p1)的作用是把函式FuncA的第一個引數固定為p1；又如functools.partial(FuncB, key1="Hello")的作用是把FuncB中的引數key1固定為“Hello"。  

    functools.partial(api_export, api_name=TENSORFLOW_API_NAME)的意思是把api_export的api_name這個引數固定為TENSORFLOW_API。其中TENSORFLOW_API_NAME = 'tensorflow'。

  api_export是實現了__call__()函式的類，不懂戳這裡，簡而言之是把類變得可以像函式一樣呼叫。

  tf_export=unctools.partial(api_export, api_name=TENSORFLOW_API_NAME)的寫法等效於：  

funcC = api_export(api_name=TENSORFLOW_API_NAME)
tf_export = funcC

對於funcC = api_export(api_name=TENSORFLOW_API_NAME)，會導致__init__(api_name=TENSORFLOW_API_NAME)被呼叫:

  def __init__(self, *args, **kwargs):
    self._names = args
    self._names_v1 = kwargs.get('v1', args)
    self._api_name = kwargs.get('api_name', TENSORFLOW_API_NAME)
    self._overrides = kwargs.get('overrides', [])
    self._allow_multiple_exports = kwargs.get('allow_multiple_exports', False)

  其中第4行self._api_name=kwargs.get('api_name', TENSORFLOW_API_NAME)的意思是獲取api_name這個引數，如果未檢測到該引數，則預設為TENSORFLOW_API_NAME。由此看，api_name這個引數傳進來和預設的值都是TENSORFLOW_API_NAME,最終的結果是self._api_name=TENSORFLOW_API_NAME。

  然後呼叫像函式一個呼叫funcC()實際上就會呼叫__call__()：

  def __call__(self, func):
    api_names_attr = API_ATTRS[self._api_name].names       -----1
    api_names_attr_v1 = API_ATTRS_V1[self._api_name].names
    # Undecorate overridden names
    for f in self._overrides:
      _, undecorated_f = tf_decorator.unwrap(f)
      delattr(undecorated_f, api_names_attr)
      delattr(undecorated_f, api_names_attr_v1)

    _, undecorated_func = tf_decorator.unwrap(func)       -----2
    self.set_attr(undecorated_func, api_names_attr, self._names)  ----3
    self.set_attr(undecorated_func, api_names_attr_v1, self._names_v1)
    return func

因此@tf_export("app.run")最終的結果是用上面這個__call__()來作為修飾器。這是一個帶引數的修飾器（真心有點複雜）！

 標註1：

  api_names_attr = API_ATTRS[self._api_name].names: 中的self._api_name即為__init__()中提到的TENSORFLOW_API_NAME。看看API_ATTRS中都有些什麼：

_Attributes = collections.namedtuple(
    'ExportedApiAttributes', ['names', 'constants'])

# Attribute values must be unique to each API.
API_ATTRS = {
    TENSORFLOW_API_NAME: _Attributes(
        '_tf_api_names',
        '_tf_api_constants'),
    ESTIMATOR_API_NAME: _Attributes(
        '_estimator_api_names',
        '_estimator_api_constants')
}

collections.namedtuple()返回具有命名欄位的元組的新子類。從“ExportedApiAttributes”可推測這是用來管理已輸出的API的屬性的。

  標註2：

    _, undecorated_func = tf_decorator.unwrap(func)

def unwrap(maybe_tf_decorator):
  """Unwraps an object into a list of TFDecorators and a final target.

  Args:
    maybe_tf_decorator: Any callable object.

  Returns:
    A tuple whose first element is an list of TFDecorator-derived objects that
    were applied to the final callable target, and whose second element is the
    final undecorated callable target. If the `maybe_tf_decorator` parameter is
    not decorated by any TFDecorators, the first tuple element will be an empty
    list. The `TFDecorator` list is ordered from outermost to innermost
    decorators.
  """
  decorators = []
  cur = maybe_tf_decorator
  while True:
    if isinstance(cur, TFDecorator):
      decorators.append(cur)
    elif hasattr(cur, '_tf_decorator'):
      decorators.append(getattr(cur, '_tf_decorator'))
    else:
      break
    cur = decorators[-1].decorated_target
  return decorators, cur

將物件展開到tfdecorator列表和最終目標列表中。undecorated_func獲得的返回物件就是我們有@tf_export修飾的函式。

標註3：self.set_attr(undecorated_func, api_names_attr, self._names) 設定屬性。

總結：@tf_export修飾器為所修改的函式取了個名字！