docstr: :param sess: active session to use for prediction (must be None for MXNet)
  :param network_input_tuples: tuple of network and corresponding input
  :return: tuple of outputs from all networks
docstr_tok: [":","param","sess",":","active","session","to","use","for","prediction","(","must","be","None","for","MXNet",")",":","param","network_input_tuples",":","tuple","of","network","and","corresponding","input",":","return",":","tuple","of","outputs","from","all","networks"]
code: def parallel_predict(
      sess: Any,
      network_input_tuples: List[
          Tuple["MxnetArchitecture", Dict[str, np.ndarray]]
      ],
  ) -> Tuple[np.ndarray, ...]:
      """
      :param sess: active session to use for prediction (must be None for MXNet)
      :param network_input_tuples: tuple of network and corresponding input
      :return: tuple of outputs from all networks
      """
      assert sess is None
      output = list()
      for net, inputs in network_input_tuples:
          output += net._predict(inputs)
      return tuple(o.asnumpy() for o in output)
  
imports: import copy
  from typing import Any, Dict, Generator, List, Tuple, Union
  import numpy
  import mxnet
  from mxnet import autograd, gluon, nd
  from mxnet.ndarray import NDArray
  from rl_coach.architectures.architecture import Architecture
  from rl_coach.architectures.mxnet_components.heads.head import LOSS_OUT_TYPE_LOSS, LOSS_OUT_TYPE_REGULARIZATION
  from rl_coach.architectures.mxnet_components import utils
  from rl_coach.architectures.mxnet_components.savers import ParameterDictSaver, OnnxSaver
  from rl_coach.base_parameters import AgentParameters
  from rl_coach.logger import screen
  from rl_coach.saver import SaverCollection
  from rl_coach.spaces import SpacesDefinition
  from rl_coach.utils import force_list, squeeze_list
label_desc: Use image node_id 15 for calling the MxnetArchitecture obj's underlying member method code with example usage: obj.parallel_predict(sess, network_input_tuples) and returns: tuple