code: def baseline_loss(
      self,
      *,
      states,
      horizons,
      internals,
      auxiliaries,
      actions,
      reward,
      reference,
  ):
      # Loss per instance
      loss = self.baseline_objective.loss(
          states=states,
          horizons=horizons,
          internals=internals,
          auxiliaries=auxiliaries,
          actions=actions,
          reward=reward,
          reference=reference,
          policy=self.baseline,
      )
  
      # Objective loss
      loss = tf.math.reduce_mean(input_tensor=loss, axis=0)
  
      dependencies = list()
      if self.separate_baseline:
          dependencies.extend(
              self.summary(
                  label="loss",
                  name="losses/baseline-objective-loss",
                  data=loss,
                  step="updates",
              )
          )
          dependencies.extend(
              self.track(
                  label="loss",
                  name="baseline-objective-loss",
                  data=loss,
              )
          )
  
          # Regularization losses
          regularization_loss = self.baseline.regularize()
          dependencies.extend(
              self.summary(
                  label="loss",
                  name="losses/baseline-regularization-loss",
                  data=regularization_loss,
                  step="updates",
              )
          )
          dependencies.extend(
              self.track(
                  label="loss",
                  name="baseline-regularization-loss",
                  data=regularization_loss,
              )
          )
          loss += regularization_loss
  
      dependencies.extend(
          self.summary(
              label="loss",
              name="losses/baseline-loss",
              data=loss,
              step="updates",
          )
      )
      dependencies.extend(
          self.track(label="loss", name="baseline-loss", data=loss)
      )
  
      with tf.control_dependencies(control_inputs=dependencies):
          return tf_util.identity(input=loss)
  
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 22 for calling the TensorforceModel obj's underlying member method code with example usage: obj.baseline_loss() and returns: tf_util