code: def core_initialize(self):
      super().core_initialize()
  
      # Preprocessed episode reward
      if self.reward_processing is not None:
          self.preprocessed_episode_return = self.variable(
              name="preprocessed-episode-return",
              spec=TensorSpec(
                  type=self.reward_spec.type,
                  shape=(self.parallel_interactions,),
              ),
              initializer="zeros",
              is_trainable=False,
              is_saved=False,
          )
  
      # Buffer index
      self.buffer_index = self.variable(
          name="buffer-index",
          spec=TensorSpec(
              type="int", shape=(self.parallel_interactions,)
          ),
          initializer="zeros",
          is_trainable=False,
          is_saved=False,
      )
  
      if (
          self.reward_horizon == "episode"
          or self.parallel_interactions > 1
          or self.config.buffer_observe == "episode"
      ):
          capacity = self.max_episode_timesteps
      else:
          capacity = (
              self.config.buffer_observe
              + self.reward_horizon.max_value()
          )
          if self.max_episode_timesteps is not None:
              capacity = min(capacity, self.max_episode_timesteps)
  
      # States/internals/auxiliaries/actions buffers
      def function(name, spec):
          shape = (self.parallel_interactions, capacity) + spec.shape
          return self.variable(
              name=(name + "-buffer"),
              spec=TensorSpec(type=spec.type, shape=shape),
              initializer="zeros",
              is_trainable=False,
              is_saved=False,
          )
  
      self.states_buffer = self.processed_states_spec.fmap(
          function=function, cls=VariableDict, with_names="states"
      )
      self.internals_buffer = self.internals_spec.fmap(
          function=function, cls=VariableDict, with_names=True
      )
      self.auxiliaries_buffer = self.auxiliaries_spec.fmap(
          function=function, cls=VariableDict, with_names="action"
      )
      self.actions_buffer = self.actions_spec.fmap(
          function=function, cls=VariableDict, with_names="actions"
      )
  
      # Terminal/reward buffer
      if self.config.buffer_observe != "episode":
          self.terminal_buffer = function(
              "terminal", self.terminal_spec
          )
          self.reward_buffer = function("reward", self.reward_spec)
  
      # Buffer start
      if (
          self.reward_horizon != "episode"
          and self.parallel_interactions == 1
          and self.config.buffer_observe != "episode"
      ):
          self.circular_buffer = True
          self.buffer_capacity = capacity
          self.buffer_start = self.variable(
              name="buffer-start",
              spec=TensorSpec(
                  type="int", shape=(self.parallel_interactions,)
              ),
              initializer="zeros",
              is_trainable=False,
              is_saved=False,
          )
      else:
          self.circular_buffer = False
  
      # Last update
      self.last_update = self.variable(
          name="last-update",
          spec=TensorSpec(type="int"),
          initializer=-self.update_frequency.max_value(),
          is_trainable=False,
          is_saved=True,
      )
  
      # Optimizer initialize given variables
      if self.advantage_in_loss:
          self.optimizer.initialize_given_variables(
              variables=self.trainable_variables
          )
      else:
          self.optimizer.initialize_given_variables(
              variables=self.policy.trainable_variables
          )
      if self.baseline_optimizer is not None:
          self.baseline_optimizer.initialize_given_variables(
              variables=self.baseline.trainable_variables
          )
  
      # Summaries and tracking
      self.register_summary(
          label="loss", name="losses/policy-objective-loss"
      )
      self.register_tracking(
          label="loss",
          name="policy-objective-loss",
          spec=TensorSpec(type="float"),
      )
      self.register_summary(
          label="loss", name="losses/policy-regularization-loss"
      )
      self.register_tracking(
          label="loss",
          name="policy-regularization-loss",
          spec=TensorSpec(type="float"),
      )
      self.register_summary(label="loss", name="losses/policy-loss")
      self.register_tracking(
          label="loss",
          name="policy-loss",
          spec=TensorSpec(type="float"),
      )
      if self.baseline_optimizer is not None or (
          self.baseline_loss_weight is not None
          and not self.baseline_loss_weight.is_constant(value=0.0)
      ):
          self.register_summary(
              label="loss", name="losses/baseline-loss"
          )
          self.register_tracking(
              label="loss",
              name="baseline-loss",
              spec=TensorSpec(type="float"),
          )
          if self.separate_baseline:
              self.register_summary(
                  label="loss", name="losses/baseline-objective-loss"
              )
              self.register_tracking(
                  label="loss",
                  name="baseline-objective-loss",
                  spec=TensorSpec(type="float"),
              )
              self.register_summary(
                  label="loss",
                  name="losses/baseline-regularization-loss",
              )
              self.register_tracking(
                  label="loss",
                  name="baseline-regularization-loss",
                  spec=TensorSpec(type="float"),
              )
  
      if self.reward_processing is not None:
          self.register_tracking(
              label="reward",
              name="preprocessed-reward",
              spec=TensorSpec(type="float"),
          )
          self.register_tracking(
              label="reward",
              name="preprocessed-episode-return",
              spec=TensorSpec(type="float"),
          )
      self.register_tracking(
          label="reward",
          name="update-return",
          spec=TensorSpec(type="float"),
      )
      if self.return_processing is not None:
          self.register_tracking(
              label="reward",
              name="update-processed-return",
              spec=TensorSpec(type="float"),
          )
      if self.estimate_advantage is not False:
          self.register_tracking(
              label="reward",
              name="update-advantage",
              spec=TensorSpec(type="float"),
          )
          if self.advantage_processing is not None:
              self.register_tracking(
                  label="reward",
                  name="update-processed-advantage",
                  spec=TensorSpec(type="float"),
              )
      if not self.gae_decay.is_constant(value=0.0):
          self.register_tracking(
              label="reward",
              name="update-gae",
              spec=TensorSpec(type="float"),
          )
  
      self.register_tracking(
          label="entropy", name="entropy", spec=TensorSpec(type="float")
      )
      self.register_tracking(
          label="kl-divergence",
          name="kl-divergence",
          spec=TensorSpec(type="float"),
      )
      if len(self.actions_spec) > 1:
          for name in self.actions_spec:
              self.register_tracking(
                  label="entropy",
                  name=("entropies/" + name),
                  spec=TensorSpec(type="float"),
              )
              self.register_tracking(
                  label="kl-divergence",
                  name=("kl-divergences/" + name),
                  spec=TensorSpec(type="float"),
              )
  
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 4 for calling the TensorforceModel obj's underlying member method code with example usage: obj.core_initialize() and returns: self