code: def core_initialize(self):
      # Timestep counter
      self.timesteps = self.variable(
          name="timesteps",
          spec=TensorSpec(type="int"),
          initializer="zeros",
          is_trainable=False,
          is_saved=True,
      )
  
      # Episode counter
      self.episodes = self.variable(
          name="episodes",
          spec=TensorSpec(type="int"),
          initializer="zeros",
          is_trainable=False,
          is_saved=True,
      )
  
      # Update counter
      self.updates = self.variable(
          name="updates",
          spec=TensorSpec(type="int"),
          initializer="zeros",
          is_trainable=False,
          is_saved=True,
      )
  
      # Episode length/return
      self.episode_length = self.variable(
          name="episode-length",
          spec=TensorSpec(
              type="int", shape=(self.parallel_interactions,)
          ),
          initializer="zeros",
          is_trainable=False,
          is_saved=False,
      )
      self.episode_return = self.variable(
          name="episode-return",
          spec=TensorSpec(
              type=self.reward_spec.type,
              shape=(self.parallel_interactions,),
          ),
          initializer="zeros",
          is_trainable=False,
          is_saved=False,
      )
  
      # Internals buffers
      def function(name, spec, initial):
          shape = (self.parallel_interactions,) + spec.shape
          reps = (self.parallel_interactions,) + tuple(
              1 for _ in range(spec.rank)
          )
          initializer = np.tile(
              np.expand_dims(initial, axis=0), reps=reps
          )
          return self.variable(
              name=(name + "-buffer"),
              spec=TensorSpec(type=spec.type, shape=shape),
              initializer=initializer,
              is_trainable=False,
              is_saved=False,
          )
  
      self.previous_internals = self.internals_spec.fmap(
          function=function,
          cls=VariableDict,
          with_names=True,
          zip_values=self.initial_internals,
      )
  
imports: from collections import OrderedDict
  import logging
  import os
  import time
  import h5py
  import numpy
  import tensorflow
  from tensorforce import TensorforceError
  from tensorforce.core import ArrayDict, Module, SignatureDict, TensorDict, TensorSpec, TensorsSpec, tf_function, tf_util, VariableDict
  from tensorforce.core.layers import Layer
label_desc: Use image node_id 10 for calling the Model obj's underlying member method code with example usage: obj.core_initialize() and returns: self