code: def get_train_examples(
      self,
      filename="train.csv",
      text_col="text",
      label_col="label",
      size=-1,
  ):
      if size == -1:
          data_df = pd.read_csv(os.path.join(self.data_dir, filename))
  
          return self._create_examples(
              data_df, "train", text_col=text_col, label_col=label_col
          )
      else:
          data_df = pd.read_csv(os.path.join(self.data_dir, filename))
          #             data_df['comment_text'] = data_df['comment_text'].apply(cleanHtml)
          return self._create_examples(
              data_df.sample(size),
              "train",
              text_col=text_col,
              label_col=label_col,
          )
  
imports: import pandas
  import os
  import torch
  from pathlib import Path
  import pickle
  from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
  from torch.utils.data.distributed import DistributedSampler
  from transformers import WEIGHTS_NAME, BertConfig, BertForSequenceClassification, BertTokenizer, XLMConfig, XLMForSequenceClassification, XLMTokenizer, XLNetConfig, XLNetForSequenceClassification, XLNetTokenizer
label_desc: Use image node_id 2 for calling the TextProcessor obj's underlying member method code with example usage: obj.get_train_examples(filename, text_col, label_col, size) and returns: self, self