parameters: (self, input_data, data_spec=None, **inference_args)
docstring: Constructs a TF graph for evaluating a random forest.
  
      Args:
        input_data: A tensor or SparseTensor or placeholder for input data.
        data_spec: A list of tf.dtype values specifying the original types of
          each column.
        **inference_args: Keyword arguments to pass through to each tree.
  
      Returns:
        The last op in the random forest inference graph.
docstring_summary: Constructs a TF graph for evaluating a random forest.
docstring_tokens: Constructs a TF graph for evaluating a random forest .
function: def inference_graph(self, input_data, data_spec=None, **inference_args):
      """Constructs a TF graph for evaluating a random forest.
  
      Args:
        input_data: A tensor or SparseTensor or placeholder for input data.
        data_spec: A list of tf.dtype values specifying the original types of
          each column.
        **inference_args: Keyword arguments to pass through to each tree.
  
      Returns:
        The last op in the random forest inference graph.
      """
      data_spec = [constants.DATA_FLOAT] if data_spec is None else data_spec
      probabilities = []
      for i in range(self.params.num_trees):
        with ops.device(self.device_assigner.get_device(i)):
          tree_data = input_data
          if self.params.bagged_features:
            tree_data = self._bag_features(i, input_data)
          probabilities.append(self.trees[i].inference_graph(
              tree_data, data_spec, **inference_args))
      with ops.device(self.device_assigner.get_device(0)):
        all_predict = array_ops.pack(probabilities)
        return math_ops.div(
            math_ops.reduce_sum(all_predict, 0), self.params.num_trees,
            name='probabilities')
function_tokens: def inference_graph ( self , input_data , data_spec = None , * * inference_args ) : data_spec = [ constants . DATA_FLOAT ] if data_spec is None else data_spec probabilities = [ ] for i in range ( self . params . num_trees ) : with ops . device ( self . device_assigner . get_device ( i ) ) : tree_data = input_data if self . params . bagged_features : tree_data = self . _bag_features ( i , input_data ) probabilities . append ( self . trees [ i ] . inference_graph ( tree_data , data_spec , * * inference_args ) ) with ops . device ( self . device_assigner . get_device ( 0 ) ) : all_predict = array_ops . pack ( probabilities ) return math_ops . div ( math_ops . reduce_sum ( all_predict , 0 ) , self . params . num_trees , name = 'probabilities' )