docstr: Run a forward pass of the network using the given input
  :param inputs: The input dictionary for the network. Key is name of the embedder.
  :return: The network output
  
  WARNING: must only call once per state since each call is assumed by LSTM to be a new time step.
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code: def _predict(
      self, inputs: Dict[str, np.ndarray]
  ) -> Tuple[NDArray, ...]:
      """
      Run a forward pass of the network using the given input
      :param inputs: The input dictionary for the network. Key is name of the embedder.
      :return: The network output
  
      WARNING: must only call once per state since each call is assumed by LSTM to be a new time step.
      """
      embedders = [
          emb.embedder_name
          for emb in self.model.nets[0].input_embedders
      ]
      nd_inputs = tuple(
          nd.array(inputs[emb], ctx=self._devices[0])
          for emb in embedders
      )
  
      assert self.middleware.__class__.__name__ != "LSTMMiddleware"
  
      output = self.model(*nd_inputs)
      return 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 13 for calling the MxnetArchitecture obj's underlying member method code with example usage: obj._predict(inputs) and returns: output