code: def __init__(
      self,
      dist_class,
      actions,
      curr_logits,
      behaviour_logits,
      advantages,
      value_fn,
      value_targets,
      vf_preds,
      cur_kl_coeff,
      entropy_coeff,
      clip_param,
      vf_clip_param,
      vf_loss_coeff,
      clip_loss=False,
  ):
      self.loss, surr_loss, kl_loss, vf_loss, ent_loss = PPOLoss(
          dist_class=dist_class,
          actions=actions,
          curr_logits=curr_logits,
          behaviour_logits=behaviour_logits,
          advantages=advantages,
          value_fn=value_fn,
          value_targets=value_targets,
          vf_preds=vf_preds,
          cur_kl_coeff=cur_kl_coeff,
          entropy_coeff=entropy_coeff,
          clip_param=clip_param,
          vf_clip_param=vf_clip_param,
          vf_loss_coeff=vf_loss_coeff,
          clip_loss=clip_loss,
      )
      self.loss = tf1.Print(self.loss, ["Worker Adapt Loss", self.loss])
  
imports: import logging
  from typing import Dict, List, Type, Union
  from ray.rllib.algorithms.ppo.ppo_tf_policy import validate_config
  from ray.rllib.evaluation.postprocessing import Postprocessing, compute_gae_for_sample_batch
  from ray.rllib.models.modelv2 import ModelV2
  from ray.rllib.models.tf.tf_action_dist import TFActionDistribution
  from ray.rllib.models.utils import get_activation_fn
  from ray.rllib.policy.dynamic_tf_policy_v2 import DynamicTFPolicyV2
  from ray.rllib.policy.eager_tf_policy_v2 import EagerTFPolicyV2
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.tf_mixins import LocalOptimizer, ModelGradients, ValueNetworkMixin, compute_gradients
  from ray.rllib.utils import try_import_tf
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.typing import TensorType
label_desc: Use image node_id 1 to create a new WorkerLoss object from inherited base classes: object with example: obj = WorkerLoss(dist_class, actions, curr_logits, behaviour_logits, advantages, value_fn, value_targets, vf_preds, cur_kl_coeff, entropy_coeff, clip_param, vf_clip_param, vf_loss_coeff, clip_loss)