@tf_export为函数取了个名字！
Tensorflow经常看到定义的函数前面加了@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是一个修饰符。修饰符的本质是一个函数
tf_export的实现在tensorflow/python/util/tf_export.py中：
```plain text tf_export = functools.partial(api_export, api_name=TENSORFLOW_API_NAME)
等号的右边的理解分两步：
1. functools.partial
1. api_export
functools.partial是偏函数,它的本质简而言之是为函数固定某些参数。如：`functools.partial(FuncA, p1)`的作用是把函数FuncA的第一个参数固定为p1；又如`functools.partial(FuncB, key1="Hello")`的作用是把FuncB中的参数key1固定为“Hello"。
`functools.partial(api_export, api_name=TENSORFLOW_API_NAME)`的意思是把api_export的api_name这个参数固定为TENSORFLOW_API。其中`TENSORFLOW_API_NAME = 'tensorflow'`。
api_export是实现了__call__()函数的类，简而言之是把类变得可以像函数一样调用。
`tf_export=functools.partial(api_export, api_name=TENSORFLOW_API_NAME)`的写法等效于：
```python
funcC = api_export(api_name=TENSORFLOW_API_NAME)
tf_export = funcC
对于funcC = api_export(api_name=TENSORFLOW_API_NAME)，会导致api_export类的 __init__(api_name=TENSORFLOW_API_NAME)被调用:
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)的意思是获取api_name这个参数，如果未检测到该参数，则默认为TENSORFLOW_API_NAME。由此看，api_name这个参数传进来和默认的值都是TENSORFLOW_API_NAME,最终的结果是self._api_name=TENSORFLOW_API_NAME。
然后调用像函数一样调用funcC()实际上就会调用__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")最终的结果是用上面这个__call__()来作为修饰器。这是一个带参数的修饰器！
标注1：
api_names_attr = API_ATTRS[self._api_name].names中的self._api_name即为__init__()中提到的TENSORFLOW_API_NAME。看看API_ATTRS中都有些什么：
_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()返回具有命名字段的元组的新子类。从“ExportedApiAttributes”可推测这是用来管理已输出的API的属性的。
标注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
将对象展开到tfdecorator列表和最终目标列表中。undecorated_func获得的返回对象就是我们有@tf_export修饰的函数。
标注3：self.set_attr(undecorated_func, api_names_attr, self._names) 设置属性。
总结：@tf_export修饰器为所修饰的函数取了个名字！
e.g. 通过@tf_export()就可以给函数重命名,比如@tf_export("nn.max_pool"),然后就可以使用tf.nn.max_pool()来调用tensorflow.python.ops.nn_ops.max_pool()函数