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,
      meta_opt,
      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
      self.model = model
      self.vf_clip_param = vf_clip_param
      self.vf_loss_coeff = vf_loss_coeff
      self.entropy_coeff = entropy_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)
  
      inner_opt = torch.optim.SGD(
          model.parameters(), lr=config["inner_lr"]
      )
      surr_losses = []
      val_losses = []
      kl_losses = []
      entropy_losses = []
      meta_losses = []
      kls = []
  
      meta_opt.zero_grad()
      for i in range(self.num_tasks):
          with higher.innerloop_ctx(
              model, inner_opt, copy_initial_weights=False
          ) as (
              fnet,
              diffopt,
          ):
              inner_kls = []
              for step in range(self.inner_adaptation_steps):
                  (
                      ppo_loss,
                      _,
                      inner_kl_loss,
                      _,
                      _,
                  ) = self.compute_losses(fnet, step, i)
                  diffopt.step(ppo_loss)
                  inner_kls.append(inner_kl_loss)
                  kls.append(inner_kl_loss.detach())
  
              # Meta Update
              (
                  ppo_loss,
                  s_loss,
                  kl_loss,
                  v_loss,
                  ent,
              ) = self.compute_losses(
                  fnet,
                  self.inner_adaptation_steps - 1,
                  i,
                  clip_loss=True,
              )
  
              inner_loss = torch.mean(
                  torch.stack(
                      [
                          a * b
                          for a, b in zip(
                              self.cur_kl_coeff[
                                  i
                                  * self.inner_adaptation_steps : (
                                      i + 1
                                  )
                                  * self.inner_adaptation_steps
                              ],
                              inner_kls,
                          )
                      ]
                  )
              )
              meta_loss = (ppo_loss + inner_loss) / self.num_tasks
              meta_loss.backward()
  
              surr_losses.append(s_loss.detach())
              kl_losses.append(kl_loss.detach())
              val_losses.append(v_loss.detach())
              entropy_losses.append(ent.detach())
              meta_losses.append(meta_loss.detach())
  
      meta_opt.step()
  
      # Stats Logging
      self.mean_policy_loss = torch.mean(torch.stack(surr_losses))
      self.mean_kl_loss = torch.mean(torch.stack(kl_losses))
      self.mean_vf_loss = torch.mean(torch.stack(val_losses))
      self.mean_entropy = torch.mean(torch.stack(entropy_losses))
      self.mean_inner_kl = kls
      self.loss = torch.sum(torch.stack(meta_losses))
      # Hacky, needed to bypass RLlib backend
      self.loss.requires_grad = True
  
imports: import logging
  from typing import Dict, List, Type, Union
  import ray
  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.torch.torch_action_dist import TorchDistributionWrapper
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.policy.torch_mixins import ValueNetworkMixin
  from ray.rllib.policy.torch_policy_v2 import TorchPolicyV2
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.numpy import convert_to_numpy
  from ray.rllib.utils.torch_utils import apply_grad_clipping
  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, meta_opt, inner_adaptation_steps, entropy_coeff, clip_param, vf_clip_param, vf_loss_coeff, use_gae)