code: def baseline_optimizer_arguments(self, states, internals, reward):
          
          arguments = dict(
              time=self.global_timestep,
              variables=self.baseline.get_variables(),
              arguments=dict(
                  states=states,
                  internals=internals,
                  reward=reward,
                  update=tf.constant(value=True),
              ),
              fn_reference=self.baseline.reference,
              fn_loss=self.fn_baseline_loss,
              # source_variables=self.network.get_variables()
          )
          if self.global_model is not None:
              arguments['global_variables'] = self.global_model.baseline.get_variables()
          return arguments
docs: Returns the baseline optimizer arguments including the time, the list of variables to  
          optimize, and various functions which the optimizer might require to perform an update  
          step.
  
          Args:
              states: Dict of state tensors.
              internals: List of prior internal state tensors.
              reward: Reward tensor.
  
          Returns:
              Baseline optimizer arguments as dict.