class_docstr: Implements DreamerV3 losses and gradient-based update logic in TensorFlow.
  
  The critic EMA-copy update step can be found in the `DreamerV3Learner` base class,
  as it is framework independent.
  
  We define 3 local TensorFlow optimizers for the sub components "world_model",
  "actor", and "critic". Each of these optimizers might use a different learning rate,
  epsilon parameter, and gradient clipping thresholds and procedures.
docstr: Create the 3 optimizers for Dreamer learning: world_model, actor, critic.
  
  The learning rates used are described in [1] and the epsilon values used here
  - albeit probably not that important - are used by the author's own
  implementation.
docstr_tok: ["Create","the","3","optimizers","for","Dreamer","learning",":","world_model",",","actor",",","critic",".","The","learning","rates","used","are","described","in","[","1","]","and","the","epsilon","values","used","here","-","albeit","probably","not","that","important","-","are","used","by","the","author","'s","own","implementation","."]
code: def configure_optimizers_for_module(
      self,
      module_id: ModuleID,
      config: DreamerV3Config = None,
      hps=None,
  ):
      """Create the 3 optimizers for Dreamer learning: world_model, actor, critic.
  
      The learning rates used are described in [1] and the epsilon values used here
      - albeit probably not that important - are used by the author's own
      implementation.
      """
      if hps is not None:
          deprecation_warning(
              old="Learner.configure_optimizers_for_module(.., hps=..)",
              help="Deprecated argument. Use `config` (AlgorithmConfig) instead.",
              error=True,
          )
  
      dreamerv3_module = self._module[module_id]
  
      # World Model optimizer.
      optim_world_model = tf.keras.optimizers.Adam(epsilon=1e-8)
      optim_world_model.build(
          dreamerv3_module.world_model.trainable_variables
      )
      params_world_model = self.get_parameters(
          dreamerv3_module.world_model
      )
      self.register_optimizer(
          module_id=module_id,
          optimizer_name="world_model",
          optimizer=optim_world_model,
          params=params_world_model,
          lr_or_lr_schedule=config.world_model_lr,
      )
  
      # Actor optimizer.
      optim_actor = tf.keras.optimizers.Adam(epsilon=1e-5)
      optim_actor.build(dreamerv3_module.actor.trainable_variables)
      params_actor = self.get_parameters(dreamerv3_module.actor)
      self.register_optimizer(
          module_id=module_id,
          optimizer_name="actor",
          optimizer=optim_actor,
          params=params_actor,
          lr_or_lr_schedule=config.actor_lr,
      )
  
      # Critic optimizer.
      optim_critic = tf.keras.optimizers.Adam(epsilon=1e-5)
      optim_critic.build(dreamerv3_module.critic.trainable_variables)
      params_critic = self.get_parameters(dreamerv3_module.critic)
      self.register_optimizer(
          module_id=module_id,
          optimizer_name="critic",
          optimizer=optim_critic,
          params=params_critic,
          lr_or_lr_schedule=config.critic_lr,
      )
  
imports: from typing import Any, Dict, Tuple
  import gymnasium
  from ray.rllib.algorithms.dreamerv3.dreamerv3 import DreamerV3Config
  from ray.rllib.algorithms.dreamerv3.dreamerv3_learner import DreamerV3Learner
  from ray.rllib.core.learner.learner import ParamDict
  from ray.rllib.core.learner.tf.tf_learner import TfLearner
  from ray.rllib.policy.sample_batch import DEFAULT_POLICY_ID, SampleBatch
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.deprecation import deprecation_warning
  from ray.rllib.utils.framework import try_import_tf, try_import_tfp
  from ray.rllib.utils.tf_utils import symlog, two_hot, clip_gradients
  from ray.rllib.utils.typing import ModuleID, TensorType
label_desc: Use image node_id 1 for calling the DreamerV3TfLearner obj's underlying member method code with example usage: obj.configure_optimizers_for_module(module_id, config, hps) without return types