docstr: Run a forward pass of the network using the given input
  :param inputs: The input for the network
  :param outputs: The output for the network, defaults to self.outputs
  :param squeeze_output: call squeeze_list on output
  :param initial_feed_dict: a dictionary to use as the initial feed_dict. other inputs will be added to this dict
  :return: The network output
  
  WARNING: must only call once per state since each call is assumed by LSTM to be a new time step.
code: def predict(
      self,
      inputs,
      outputs=None,
      squeeze_output=True,
      initial_feed_dict=None,
  ):
      """
      Run a forward pass of the network using the given input
      :param inputs: The input for the network
      :param outputs: The output for the network, defaults to self.outputs
      :param squeeze_output: call squeeze_list on output
      :param initial_feed_dict: a dictionary to use as the initial feed_dict. other inputs will be added to this dict
      :return: The network output
  
      WARNING: must only call once per state since each call is assumed by LSTM to be a new time step.
      """
      feed_dict = self.create_feed_dict(inputs)
      if initial_feed_dict:
          feed_dict.update(initial_feed_dict)
      if outputs is None:
          outputs = self.outputs
  
      if self.middleware.__class__.__name__ == "LSTMMiddleware":
          feed_dict[self.middleware.c_in] = self.curr_rnn_c_in
          feed_dict[self.middleware.h_in] = self.curr_rnn_h_in
  
          output, (
              self.curr_rnn_c_in,
              self.curr_rnn_h_in,
          ) = self.sess.run(
              [outputs, self.middleware.state_out], feed_dict=feed_dict
          )
      else:
          output = self.sess.run(outputs, feed_dict)
  
      if squeeze_output:
          output = squeeze_list(output)
      return output
  
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 17 for calling the TensorFlowArchitecture obj's underlying member method code with example usage: obj.predict(inputs, outputs, squeeze_output, initial_feed_dict) and returns: output