code: def __init__(
      self,
      model_type: str = "GPT2",
      model_name: str = "gpt2",
      load_path: str = "",
      use_auth_token: Union[bool, str] = None,
      trust_remote_code: bool = False,
  ):
      self.adaptor = get_adaptor(model_type)
      model_class = AutoModelForCausalLM
  
      super().__init__(
          model_type,
          model_name,
          model_class,
          use_auth_token=use_auth_token,
          load_path=load_path,
          trust_remote_code=trust_remote_code,
      )
  
      self._data_collator = default_data_collator
  
      self._t_data_file_type = ["text", "csv"]
  
      self._type = "gen"
  
      self._pipeline_class = TextGenerationPipeline
  
imports: from dataclasses import dataclass
  from typing import List, Union
  from datasets import Dataset
  from transformers import AutoModelForCausalLM, default_data_collator, TextGenerationPipeline
  from happytransformer.adaptors import get_adaptor
  from happytransformer.args import GENEvalArgs, GENTrainArgs
  from happytransformer.fine_tuning_util import csv_tok_text_gen_mlm, EvalResult, tok_text_gen_mlm
  from happytransformer.happy_transformer import HappyTransformer
label_desc: Use image node_id 1 to create a new HappyGeneration object from inherited base classes: HappyTransformer with example: obj = HappyGeneration(model_type, model_name, load_path, use_auth_token, trust_remote_code)