code: def compute_updated_variables(self, loss, network_vars):
      grad = tf.gradients(loss, list(network_vars.values()))
      adapted_vars = {}
      for i, tup in enumerate(network_vars.items()):
          name, var = tup
          if grad[i] is None:
              adapted_vars[name] = var
          else:
              adapted_vars[name] = (
                  var - self.config["inner_lr"] * grad[i]
              )
      return adapted_vars
  
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 3 for calling the MAMLLoss obj's underlying member method code with example usage: obj.compute_updated_variables(loss, network_vars) and returns: adapted_vars