code: def build(self, framework):
      if framework == "torch":
          return CustomTorchTokenizer(self)
      else:
          return CustomTfTokenizer(self)
  
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 for calling the CustomTokenizerConfig obj's underlying member method code with example usage: obj.build(framework) and returns: CustomTorchTokenizer, CustomTfTokenizer