docstr: :param sess: active session to use for prediction
  :param network_input_tuples: tuple of network and corresponding input
  :return: list of outputs from all networks
code: def parallel_predict(
      sess: Any,
      network_input_tuples: List[
          Tuple["TensorFlowArchitecture", Dict[str, np.ndarray]]
      ],
  ) -> List[np.ndarray]:
      """
      :param sess: active session to use for prediction
      :param network_input_tuples: tuple of network and corresponding input
      :return: list of outputs from all networks
      """
      feed_dict = {}
      fetches = []
  
      for network, input in network_input_tuples:
          feed_dict.update(network.create_feed_dict(input))
          fetches += network.outputs
  
      outputs = sess.run(fetches, feed_dict)
  
      return outputs
  
imports: import os
  import time
  from typing import Any, List, Tuple, Dict
  import numpy
  import tensorflow
  from rl_coach.architectures.architecture import Architecture
  from rl_coach.architectures.tensorflow_components.savers import GlobalVariableSaver
  from rl_coach.base_parameters import AgentParameters, DistributedTaskParameters
  from rl_coach.core_types import GradientClippingMethod
  from rl_coach.saver import SaverCollection
  from rl_coach.spaces import SpacesDefinition
  from rl_coach.utils import force_list, squeeze_list, start_shell_command_and_wait
label_desc: Use image node_id 18 for calling the TensorFlowArchitecture obj's underlying member method code with example usage: obj.parallel_predict(sess, network_input_tuples) and returns: outputs