code: def get_architecture(self):
      if self.state_preprocessing.is_singleton():
          architecture = "State-preprocessing:  {}\n".format(
              self.state_preprocessing.singleton()
              .get_architecture()
              .replace("\n", "\n    ")
          )
      else:
          architecture = ""
          for name, preprocessor in self.state_preprocessing.items():
              architecture += "    {}:  {}\n".format(
                  name,
                  preprocessor.get_architecture().replace(
                      "\n", "\n    "
                  ),
              )
          if len(architecture) > 0:
              architecture = "State-preprocessing:\n" + architecture
      architecture = "Policy:\n    {}".format(
          self.policy.get_architecture().replace("\n", "\n    ")
      )
      if self.separate_baseline:
          architecture += "\nBaseline:\n    {}".format(
              self.baseline.get_architecture().replace("\n", "\n    ")
          )
      elif (
          self.predict_horizon_values
          or self.baseline_objective is not None
      ):
          architecture += "\nBaseline:  policy used as baseline"
      return architecture
  
imports: import logging
  import tensorflow
  from tensorforce import TensorforceError
  from tensorforce.core import ModuleDict, memory_modules, optimizer_modules, parameter_modules, SignatureDict, TensorDict, TensorSpec, TensorsSpec, tf_function, tf_util, VariableDict
  from tensorforce.core.models import Model
  from tensorforce.core.networks import Preprocessor
  from tensorforce.core.objectives import objective_modules
  from tensorforce.core.policies import policy_modules, StochasticPolicy
label_desc: Use image node_id 2 for calling the TensorforceModel obj's underlying member method code with example usage: obj.get_architecture() and returns: architecture