code: def _translate_weights_to_torch(self, weights_dict, map_):
      model_dict = {
          map_[k]: convert_to_torch_tensor(
              np.transpose(v) if re.search("kernel", k) else v
          )
          for k, v in weights_dict.items()
          if re.search(
              "default_policy/(actor_(hidden_0|out)|sequential(_1)?)/",
              k,
          )
      }
      model_dict[
          "policy_model.action_out_squashed.low_action"
      ] = convert_to_torch_tensor(np.array([0.0]))
      model_dict[
          "policy_model.action_out_squashed.action_range"
      ] = convert_to_torch_tensor(np.array([1.0]))
      return model_dict
  
imports: import copy
  import re
  import unittest
  import gymnasium
  import numpy
  from gymnasium.spaces import Box
  from rllib_ddpg.ddpg import DDPGConfig
  import ray
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.utils.framework import try_import_tf, try_import_torch
  from ray.rllib.utils.numpy import convert_to_numpy, fc, huber_loss, l2_loss, relu, sigmoid
  from ray.rllib.utils.replay_buffers.utils import patch_buffer_with_fake_sampling_method
  from ray.rllib.utils.spaces.simplex import Simplex
  from ray.rllib.utils.test_utils import check, check_compute_single_action, check_train_results, framework_iterator
  from ray.rllib.utils.torch_utils import convert_to_torch_tensor
label_desc: Use image node_id 8 for calling the TestDDPG obj's underlying member method code with example usage: obj._translate_weights_to_torch(weights_dict, map_) and returns: model_dict