docstr: :param sess: active session to use for prediction
  :param network_input_tuples: tuple of network and corresponding input
  :return: list or tuple of outputs from all networks
docstr_tok: [":","param","sess",":","active","session","to","use","for","prediction",":","param","network_input_tuples",":","tuple","of","network","and","corresponding","input",":","return",":","list","or","tuple","of","outputs","from","all","networks"]
code: def parallel_predict(
      sess: Any,
      network_input_tuples: List[
          Tuple["Architecture", Dict[str, np.ndarray]]
      ],
  ) -> Tuple[np.ndarray, ...]:
      """
      :param sess: active session to use for prediction
      :param network_input_tuples: tuple of network and corresponding input
      :return: list or tuple of outputs from all networks
      """
      raise NotImplementedError
  
imports: from typing import Any, Dict, List, Tuple
  import numpy
  from rl_coach.base_parameters import AgentParameters
  from rl_coach.saver import SaverCollection
  from rl_coach.spaces import SpacesDefinition
label_desc: Use image node_id 4 for calling the Architecture obj's underlying member method code with example usage: obj.parallel_predict(sess, network_input_tuples) without return types