code: def __init__(
      self,
      model,
      config,
      dist_class,
      value_targets,
      advantages,
      actions,
      behaviour_logits,
      vf_preds,
      cur_kl_coeff,
      policy_vars,
      obs,
      num_tasks,
      split,
      inner_adaptation_steps=1,
      entropy_coeff=0,
      clip_param=0.3,
      vf_clip_param=0.1,
      vf_loss_coeff=1.0,
      use_gae=True,
  ):
      self.config = config
      self.num_tasks = num_tasks
      self.inner_adaptation_steps = inner_adaptation_steps
      self.clip_param = clip_param
      self.dist_class = dist_class
      self.cur_kl_coeff = cur_kl_coeff
  
      # Split episode tensors into [inner_adaptation_steps+1, num_tasks, -1]
      self.obs = self.split_placeholders(obs, split)
      self.actions = self.split_placeholders(actions, split)
      self.behaviour_logits = self.split_placeholders(
          behaviour_logits, split
      )
      self.advantages = self.split_placeholders(advantages, split)
      self.value_targets = self.split_placeholders(value_targets, split)
      self.vf_preds = self.split_placeholders(vf_preds, split)
  
      #  Construct name to tensor dictionary for easier indexing
      self.policy_vars = {}
      for var in policy_vars:
          self.policy_vars[var.name] = var
  
      # Calculate pi_new for PPO
      pi_new_logits, current_policy_vars, value_fns = [], [], []
      for i in range(self.num_tasks):
          pi_new, value_fn = self.feed_forward(
              self.obs[0][i],
              self.policy_vars,
              policy_config=config["model"],
          )
          pi_new_logits.append(pi_new)
          value_fns.append(value_fn)
          current_policy_vars.append(self.policy_vars)
  
      inner_kls = []
      inner_ppo_loss = []
  
      # Recompute weights for inner-adaptation (same weights as workers)
      for step in range(self.inner_adaptation_steps):
          kls = []
          for i in range(self.num_tasks):
              # PPO Loss Function (only Surrogate)
              ppo_loss, _, kl_loss, _, _ = PPOLoss(
                  dist_class=dist_class,
                  actions=self.actions[step][i],
                  curr_logits=pi_new_logits[i],
                  behaviour_logits=self.behaviour_logits[step][i],
                  advantages=self.advantages[step][i],
                  value_fn=value_fns[i],
                  value_targets=self.value_targets[step][i],
                  vf_preds=self.vf_preds[step][i],
                  cur_kl_coeff=0.0,
                  entropy_coeff=entropy_coeff,
                  clip_param=clip_param,
                  vf_clip_param=vf_clip_param,
                  vf_loss_coeff=vf_loss_coeff,
                  clip_loss=False,
              )
              adapted_policy_vars = self.compute_updated_variables(
                  ppo_loss, current_policy_vars[i]
              )
              pi_new_logits[i], value_fns[i] = self.feed_forward(
                  self.obs[step + 1][i],
                  adapted_policy_vars,
                  policy_config=config["model"],
              )
              current_policy_vars[i] = adapted_policy_vars
              kls.append(kl_loss)
              inner_ppo_loss.append(ppo_loss)
  
          self.kls = kls
          inner_kls.append(kls)
  
      mean_inner_kl = tf.stack(
          [tf.reduce_mean(tf.stack(inner_kl)) for inner_kl in inner_kls]
      )
      self.mean_inner_kl = mean_inner_kl
  
      ppo_obj = []
      for i in range(self.num_tasks):
          (
              ppo_loss,
              surr_loss,
              kl_loss,
              val_loss,
              entropy_loss,
          ) = PPOLoss(
              dist_class=dist_class,
              actions=self.actions[self.inner_adaptation_steps][i],
              curr_logits=pi_new_logits[i],
              behaviour_logits=self.behaviour_logits[
                  self.inner_adaptation_steps
              ][i],
              advantages=self.advantages[self.inner_adaptation_steps][
                  i
              ],
              value_fn=value_fns[i],
              value_targets=self.value_targets[
                  self.inner_adaptation_steps
              ][i],
              vf_preds=self.vf_preds[self.inner_adaptation_steps][i],
              cur_kl_coeff=0.0,
              entropy_coeff=entropy_coeff,
              clip_param=clip_param,
              vf_clip_param=vf_clip_param,
              vf_loss_coeff=vf_loss_coeff,
              clip_loss=True,
          )
          ppo_obj.append(ppo_loss)
      self.mean_policy_loss = surr_loss
      self.mean_kl = kl_loss
      self.mean_vf_loss = val_loss
      self.mean_entropy = entropy_loss
      self.inner_kl_loss = tf.reduce_mean(
          tf.multiply(self.cur_kl_coeff, mean_inner_kl)
      )
      self.loss = (
          tf.reduce_mean(tf.stack(ppo_obj, axis=0)) + self.inner_kl_loss
      )
      self.loss = tf1.Print(
          self.loss,
          ["Meta-Loss", self.loss, "Inner KL", self.mean_inner_kl],
      )
  
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 MAMLLoss object from inherited base classes: object with example: obj = MAMLLoss(model, config, dist_class, value_targets, advantages, actions, behaviour_logits, vf_preds, cur_kl_coeff, policy_vars, obs, num_tasks, split, inner_adaptation_steps, entropy_coeff, clip_param, vf_clip_param, vf_loss_coeff, use_gae)