code: def __init__(self, config) -> None:
      TorchModel.__init__(self, config)
      Encoder.__init__(self, config)
      self.net = nn.Sequential(
          nn.Linear(config.input_dims[0], config.output_dims[0]),
      )
  
imports: import argparse
  import os
  import ray
  from ray import air, tune
  from ray.tune.registry import register_env
  from ray.rllib.examples.env.repeat_after_me_env import RepeatAfterMeEnv
  from ray.rllib.examples.env.repeat_initial_obs_env import RepeatInitialObsEnv
  from ray.rllib.core.rl_module.rl_module import SingleAgentRLModuleSpec
  from ray.rllib.policy.sample_batch import SampleBatch
  from dataclasses import dataclass
  from ray.rllib.core.models.specs.specs_dict import SpecDict
  from ray.rllib.core.models.specs.specs_base import TensorSpec
  from ray.rllib.core.models.base import Encoder, ENCODER_OUT
  from ray.rllib.core.models.torch.base import TorchModel
  from ray.rllib.core.models.tf.base import TfModel
  from ray.rllib.algorithms.ppo.ppo import PPOConfig
  from ray.rllib.algorithms.ppo.ppo_catalog import PPOCatalog
  from ray.rllib.utils.framework import try_import_tf, try_import_torch
  from ray.rllib.utils.test_utils import check_learning_achieved
  from ray.rllib.core.models.configs import ModelConfig
label_desc: Use image node_id 1 to create a new CustomTorchTokenizer object from inherited base classes: TorchModel, Encoder with example: obj = CustomTorchTokenizer(config)