parameters: (self, input_statistics)
docstring: Save templates for components, which can then be used repeatedly.
      This method is called every time a new graph is created. It's safe to start
      adding ops to the current default graph here, but the graph should be
      constructed from scratch.
      Args:
        input_statistics: A math_utils.InputStatistics object.
docstring_summary: Save templates for components, which can then be used repeatedly.
      This method is called every time a new graph is created. It's safe to start
      adding ops to the current default graph here, but the graph should be
      constructed from scratch.
      Args:
        input_statistics: A math_utils.InputStatistics object.
docstring_tokens: Save templates for components which can then be used repeatedly . This method is called every time a new graph is created . It s safe to start adding ops to the current default graph here but the graph should be constructed from scratch . Args : input_statistics : A math_utils . InputStatistics object .
function: def initialize_graph(self, input_statistics):
      """Save templates for components, which can then be used repeatedly.
      This method is called every time a new graph is created. It's safe to start
      adding ops to the current default graph here, but the graph should be
      constructed from scratch.
      Args:
        input_statistics: A math_utils.InputStatistics object.
      """
      super(_LSTMModel, self).initialize_graph(input_statistics=input_statistics)
      self._lstm_cell = tf.nn.rnn_cell.LSTMCell(num_units=self._num_units)
      # Create templates so we don't have to worry about variable reuse.
      self._lstm_cell_run = tf.make_template(
          name_="lstm_cell",
          func_=self._lstm_cell,
          create_scope_now_=True)
      # Transforms LSTM output into mean predictions.
      self._predict_from_lstm_output = tf.make_template(
          name_="predict_from_lstm_output",
          func_=lambda inputs: tf.layers.dense(inputs=inputs, units=self.num_features),
          create_scope_now_=True)
function_tokens: def initialize_graph ( self , input_statistics ) : super ( _LSTMModel , self ) . initialize_graph ( input_statistics = input_statistics ) self . _lstm_cell = tf . nn . rnn_cell . LSTMCell ( num_units = self . _num_units ) # Create templates so we don't have to worry about variable reuse. self . _lstm_cell_run = tf . make_template ( name_ = "lstm_cell" , func_ = self . _lstm_cell , create_scope_now_ = True ) # Transforms LSTM output into mean predictions. self . _predict_from_lstm_output = tf . make_template ( name_ = "predict_from_lstm_output" , func_ = lambda inputs : tf . layers . dense ( inputs = inputs , units = self . num_features ) , create_scope_now_ = True )