docstr: Constructs the model using `network_parameters` and sets `input_embedders`, `middleware`,
  `output_heads`, `outputs`, `losses`, `total_loss`, `adaptive_learning_rate_scheme`,
  `current_learning_rate`, and `optimizer`.
  
  :return: A list of the model's weights
code: def get_model(self) -> List:
      """
      Constructs the model using `network_parameters` and sets `input_embedders`, `middleware`,
      `output_heads`, `outputs`, `losses`, `total_loss`, `adaptive_learning_rate_scheme`,
      `current_learning_rate`, and `optimizer`.
  
      :return: A list of the model's weights
      """
      raise NotImplementedError
  
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 2 for calling the TensorFlowArchitecture obj's underlying member method code with example usage: obj.get_model() without return types