code: def _get_data(self, filepath, test_data=False):
      contexts = []
      labels = []
      with open(filepath, newline="", encoding="utf-8") as csv_file:
          reader = csv.DictReader(csv_file)
          for row in reader:
              contexts.append(row["text"])
              if not test_data:
                  labels.append(int(row["label"]))
      csv_file.close()
  
      if not test_data:
          return contexts, labels
      return contexts
  
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 7 for calling the HappyTextClassification obj's underlying member method code with example usage: obj._get_data(filepath, test_data) and returns: contexts, contexts,  labels