code: def compute_losses(
      self, model, inner_adapt_iter, task_iter, clip_loss=False
  ):
      obs = self.obs[inner_adapt_iter][task_iter]
      obs_dict = {"obs": obs, "obs_flat": obs}
      curr_logits, _ = model.forward(obs_dict, None, None)
      value_fns = model.value_function()
      ppo_loss, surr_loss, kl_loss, val_loss, ent_loss = PPOLoss(
          dist_class=self.dist_class,
          actions=self.actions[inner_adapt_iter][task_iter],
          curr_logits=curr_logits,
          behaviour_logits=self.behaviour_logits[inner_adapt_iter][
              task_iter
          ],
          advantages=self.advantages[inner_adapt_iter][task_iter],
          value_fn=value_fns,
          value_targets=self.value_targets[inner_adapt_iter][task_iter],
          vf_preds=self.vf_preds[inner_adapt_iter][task_iter],
          cur_kl_coeff=0.0,
          entropy_coeff=self.entropy_coeff,
          clip_param=self.clip_param,
          vf_clip_param=self.vf_clip_param,
          vf_loss_coeff=self.vf_loss_coeff,
          clip_loss=clip_loss,
      )
      return ppo_loss, surr_loss, kl_loss, val_loss, ent_loss
  
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 2 for calling the MAMLLoss obj's underlying member method code with example usage: obj.compute_losses(model, inner_adapt_iter, task_iter, clip_loss) and returns: ppo_loss,  surr_loss,  kl_loss,  val_loss,  ent_loss