How to use decorator_func method in locust

Best Python code snippet using locust

tf_decorator.py

Source:tf_decorator.py

1# Copyright 2017 The TensorFlow Authors. All Rights Reserved.2#3# Licensed under the Apache License, Version 2.0 (the "License");4# you may not use this file except in compliance with the License.5# You may obtain a copy of the License at6#7#     http://www.apache.org/licenses/LICENSE-2.08#9# Unless required by applicable law or agreed to in writing, software10# distributed under the License is distributed on an "AS IS" BASIS,11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.12# See the License for the specific language governing permissions and13# limitations under the License.14# ==============================================================================15"""Base TFDecorator class and utility functions for working with decorators.16There are two ways to create decorators that TensorFlow can introspect into.17This is important for documentation generation purposes, so that function18signatures aren't obscured by the (*args, **kwds) signature that decorators19often provide.201. Call `tf_decorator.make_decorator` on your wrapper function. If your21decorator is stateless, or can capture all of the variables it needs to work22with through lexical closure, this is the simplest option. Create your wrapper23function as usual, but instead of returning it, return24`tf_decorator.make_decorator(your_wrapper)`. This will attach some decorator25introspection metadata onto your wrapper and return it.26Example:27  def print_hello_before_calling(target):28    def wrapper(*args, **kwargs):29      print('hello')30      return target(*args, **kwargs)31    return tf_decorator.make_decorator(wrapper)322. Derive from TFDecorator. If your decorator needs to be stateful, you can33implement it in terms of a TFDecorator. Store whatever state you need in your34derived class, and implement the `__call__` method to do your work before35calling into your target. You can retrieve the target via36`super(MyDecoratorClass, self).decorated_target`, and call it with whatever37parameters it needs.38Example:39  class CallCounter(tf_decorator.TFDecorator):40    def __init__(self, target):41      super(CallCounter, self).__init__('count_calls', target)42      self.call_count = 043    def __call__(self, *args, **kwargs):44      self.call_count += 145      return super(CallCounter, self).decorated_target(*args, **kwargs)46  def count_calls(target):47    return CallCounter(target)48"""49from __future__ import absolute_import50from __future__ import division51from __future__ import print_function52import functools as _functools53import inspect as _inspect54def make_decorator(target,55                   decorator_func,56                   decorator_name=None,57                   decorator_doc='',58                   decorator_argspec=None):59  """Make a decorator from a wrapper and a target.60  Args:61    target: The final callable to be wrapped.62    decorator_func: The wrapper function.63    decorator_name: The name of the decorator. If `None`, the name of the64      function calling make_decorator.65    decorator_doc: Documentation specific to this application of66      `decorator_func` to `target`.67    decorator_argspec: The new callable signature of this decorator.68  Returns:69    The `decorator_func` argument with new metadata attached.70  """71  if decorator_name is None:72    decorator_name = _inspect.stack()[1][3]  # Caller's name.73  decorator = TFDecorator(decorator_name, target, decorator_doc,74                          decorator_argspec)75  setattr(decorator_func, '_tf_decorator', decorator)76  decorator_func.__name__ = target.__name__77  decorator_func.__module__ = target.__module__78  decorator_func.__doc__ = decorator.__doc__79  decorator_func.__wrapped__ = target80  return decorator_func81def unwrap(maybe_tf_decorator):82  """Unwraps an object into a list of TFDecorators and a final target.83  Args:84    maybe_tf_decorator: Any callable object.85  Returns:86    A tuple whose first element is an list of TFDecorator-derived objects that87    were applied to the final callable target, and whose second element is the88    final undecorated callable target. If the `maybe_tf_decorator` parameter is89    not decorated by any TFDecorators, the first tuple element will be an empty90    list. The `TFDecorator` list is ordered from outermost to innermost91    decorators.92  """93  decorators = []94  cur = maybe_tf_decorator95  while True:96    if isinstance(cur, TFDecorator):97      decorators.append(cur)98    elif hasattr(cur, '_tf_decorator'):99      decorators.append(getattr(cur, '_tf_decorator'))100    else:101      break102    cur = decorators[-1].decorated_target103  return decorators, cur104class TFDecorator(object):105  """Base class for all TensorFlow decorators.106  TFDecorator captures and exposes the wrapped target, and provides details107  about the current decorator.108  """109  def __init__(self,110               decorator_name,111               target,112               decorator_doc='',113               decorator_argspec=None):114    self._decorated_target = target115    self._decorator_name = decorator_name116    self._decorator_doc = decorator_doc117    self._decorator_argspec = decorator_argspec118    self.__name__ = target.__name__119    if self._decorator_doc:120      self.__doc__ = self._decorator_doc121    elif target.__doc__:122      self.__doc__ = target.__doc__123    else:124      self.__doc__ = ''125  def __get__(self, obj, objtype):126    return _functools.partial(self.__call__, obj)127  def __call__(self, *args, **kwargs):128    return self._decorated_target(*args, **kwargs)129  @property130  def decorated_target(self):131    return self._decorated_target132  @property133  def decorator_name(self):134    return self._decorator_name135  @property136  def decorator_doc(self):137    return self._decorator_doc138  @property139  def decorator_argspec(self):...

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