text: # Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  from __future__ import print_function
  
  import six
  
  from collections import defaultdict
  from paddle.fluid import core
  from paddle.fluid import framework
  
  
  class Tracer(core.Tracer):
      """
      :api_attr: imperative
      
      Tracer is used to execute and record the operators executed, to construct the 
      computation graph in dygraph model. Tracer has two mode, :code:`train_mode`
      and :code:`eval_mode`. In :code:`train_mode`, Tracer would add backward network 
      automatically and perform AutoGrad by method :code:`loss.backward()`. 
      In :code:`eval_mode`, Tracer would not add backward network.
  
      This is a low level API, users don't need to use it directly.
      """
  
      def __init__(self):
          super(Tracer, self).__init__()
  
          self._train_mode = True
  
      def trace_op(self,
                   type,
                   inputs,
                   outputs,
                   attrs,
                   stop_gradient=False,
                   inplace_map=None):
          self.trace(type, inputs, outputs, attrs,
                     framework._current_expected_place(), self._has_grad and
                     not stop_gradient, inplace_map if inplace_map else {})
  
      def train_mode(self):
          self._train_mode = True
  
      def eval_mode(self):
          self._train_mode = False
  