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 np.array([v])
              if re.search("log_alpha", k)
              else v
          )
          for i, (k, v) in enumerate(weights_dict.items())
          if i < 13
      }
  
      return model_dict
  
imports: import gymnasium
  from gymnasium.spaces import Box, Dict, Discrete, Tuple
  import numpy
  import re
  import unittest
  import ray
  from ray.rllib.algorithms import sac
  from ray.rllib.algorithms.sac.sac_tf_policy import sac_actor_critic_loss
  from ray.rllib.algorithms.sac.sac_torch_policy import actor_critic_loss
  from ray.rllib.examples.env.random_env import RandomEnv
  from ray.rllib.examples.models.batch_norm_model import KerasBatchNormModel, TorchBatchNormModel
  from ray.rllib.models.catalog import ModelCatalog
  from ray.rllib.models.tf.tf_action_dist import Dirichlet
  from ray.rllib.models.torch.torch_action_dist import TorchDirichlet
  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 fc, huber_loss, relu
  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
  from ray import tune
  from ray.rllib.utils.replay_buffers.utils import patch_buffer_with_fake_sampling_method
label_desc: Use image node_id 8 for calling the TestSAC obj's underlying member method code with example usage: obj._translate_weights_to_torch(weights_dict, map_) and returns: model_dict