parameters: (self)
return_statement: return (
          # Keeps track of the time associated with this state for error checking.
          tf.zeros([], dtype=tf.int64),
          # The previous observation or prediction.
          tf.zeros([self.num_features], dtype=self.dtype),
          # The most recently seen exogenous features.
          tf.zeros(self._get_exogenous_embedding_shape(), dtype=self.dtype),
          # The state of the RNNCell (batch dimension removed since this parent
          # class will broadcast).
          [tf.squeeze(state_element, axis=0)
           for state_element
           in self._lstm_cell.zero_state(batch_size=1, dtype=self.dtype)])
docstring: Return initial state for the time series model.
docstring_summary: Return initial state for the time series model.
docstring_tokens: Return initial state for the time series model .
function: def get_start_state(self):
      """Return initial state for the time series model."""
      return (
          # Keeps track of the time associated with this state for error checking.
          tf.zeros([], dtype=tf.int64),
          # The previous observation or prediction.
          tf.zeros([self.num_features], dtype=self.dtype),
          # The most recently seen exogenous features.
          tf.zeros(self._get_exogenous_embedding_shape(), dtype=self.dtype),
          # The state of the RNNCell (batch dimension removed since this parent
          # class will broadcast).
          [tf.squeeze(state_element, axis=0)
           for state_element
           in self._lstm_cell.zero_state(batch_size=1, dtype=self.dtype)])
function_tokens: def get_start_state ( self ) : return ( # Keeps track of the time associated with this state for error checking. tf . zeros ( [ ] , dtype = tf . int64 ) , # The previous observation or prediction. tf . zeros ( [ self . num_features ] , dtype = self . dtype ) , # The most recently seen exogenous features. tf . zeros ( self . _get_exogenous_embedding_shape ( ) , dtype = self . dtype ) , # The state of the RNNCell (batch dimension removed since this parent # class will broadcast). [ tf . squeeze ( state_element , axis = 0 ) for state_element in self . _lstm_cell . zero_state ( batch_size = 1 , dtype = self . dtype ) ] )