code: def feed_forward(self, obs, policy_vars, policy_config):
      # Hacky for now, reconstruct FC network with adapted weights
      # @mluo: TODO for any network
      def fc_network(
          inp,
          network_vars,
          hidden_nonlinearity,
          output_nonlinearity,
          policy_config,
      ):
          bias_added = False
          x = inp
          for name, param in network_vars.items():
              if "kernel" in name:
                  x = tf.matmul(x, param)
              elif "bias" in name:
                  x = tf.add(x, param)
                  bias_added = True
              else:
                  raise NameError
  
              if bias_added:
                  if "out" not in name:
                      x = hidden_nonlinearity(x)
                  elif "out" in name:
                      x = output_nonlinearity(x)
                  else:
                      raise NameError
                  bias_added = False
          return x
  
      policyn_vars = {}
      valuen_vars = {}
      log_std = None
      for name, param in policy_vars.items():
          if "value" in name:
              valuen_vars[name] = param
          elif "log_std" in name:
              log_std = param
          else:
              policyn_vars[name] = param
  
      output_nonlinearity = tf.identity
      hidden_nonlinearity = get_activation_fn(
          policy_config["fcnet_activation"]
      )
  
      pi_new_logits = fc_network(
          obs,
          policyn_vars,
          hidden_nonlinearity,
          output_nonlinearity,
          policy_config,
      )
      if log_std is not None:
          pi_new_logits = tf.concat(
              [pi_new_logits, 0.0 * pi_new_logits + log_std], 1
          )
      value_fn = fc_network(
          obs,
          valuen_vars,
          hidden_nonlinearity,
          output_nonlinearity,
          policy_config,
      )
  
      return pi_new_logits, tf.reshape(value_fn, [-1])
  
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 2 for calling the MAMLLoss obj's underlying member method code with example usage: obj.feed_forward(obs, policy_vars, policy_config) and returns: pi_new_logits,  tf, x