code: def classify_text(self, text: str) -> TextClassificationResult:
      # loads pipeline if it hasn't been loaded already.
      self._load_pipeline()
  
      # Blocking allowing a for a list of strings
      if not isinstance(text, str):
          raise ValueError(
              'the "text" argument must be a single string'
          )
      results = self._pipeline(text)
      # we do not support predicting a list of  texts, so only first prediction is relevant
      first_result = results[0]
  
      return TextClassificationResult(
          label=first_result["label"], score=first_result["score"]
      )
  
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 2 for calling the HappyTextClassification obj's underlying member method code with example usage: obj.classify_text(text) and returns: TextClassificationResult