code: def __init__(
      self,
      model_type="DISTILBERT",
      model_name="distilbert-base-uncased",
      num_labels: int = 2,
      load_path: str = "",
      use_auth_token: Union[bool, str] = None,
      trust_remote_code: bool = False,
  ):
      self._num_labels = num_labels
      self.adaptor = get_adaptor(model_type)
      model_class = AutoModelForSequenceClassification
  
      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._pipeline_class = TextClassificationPipeline
  
      self._data_collator = DataCollatorWithPadding(self.tokenizer)
      self._t_data_file_type = ["csv"]
      self._type = "tc"
  
imports: import csv
  from typing import Union
  from dataclasses import dataclass
  from datasets import Dataset
  from tqdm import tqdm
  from transformers import AutoConfig, AutoModelForSequenceClassification, AutoTokenizer, DataCollatorWithPadding, TextClassificationPipeline
  from happytransformer.adaptors import get_adaptor
  from happytransformer.args import TCEvalArgs, TCTestArgs, TCTrainArgs
  from happytransformer.fine_tuning_util import EvalResult
  from happytransformer.happy_transformer import HappyTransformer
label_desc: Use image node_id 1 to create a new HappyTextClassification object from inherited base classes: HappyTransformer with example: obj = HappyTextClassification(model_type, model_name, num_labels, load_path, use_auth_token, trust_remote_code)