code: def df_to_dataset(df, tokenizer, max_seq_length):
      bert_df = pd.DataFrame(
          {
              "id": range(len(df)),
              "label": df.iloc[:, 1],
              "alpha": ["a"] * df.shape[0],
              "text": df.iloc[:, 0].replace(r"\n", " ", regex=True),
          }
      )
      examples = []
      for i, line in enumerate(bert_df.T.to_dict().values()):
          guid = "%s-%s" % ("train", i)
          text_a = line["text"]
          label = line["label"]
          examples.append(
              InputExample(
                  guid=guid, text_a=text_a, text_b=None, label=label
              )
          )
  
      label_map = {label: i for i, label in enumerate([0, 1])}
      examples = [
          (example, label_map, tokenizer, max_seq_length)
          for example in examples
      ]
  
      with closing(Pool(10)) as p:
          features = list(
              tqdm(
                  p.imap(example_to_feature, examples, chunksize=100),
                  total=len(examples),
              )
          )
          p.terminate()
  
      return features_to_dataset(features)
  
imports: from __future__ import print_function, unicode_literals
  import random
  from contextlib import closing
  from multiprocessing import Pool
  import numpy
  import pandas
  import torch
  from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset, random_split
  from torchtext.legacy import data
  from tqdm import tqdm
label_desc: Use image node_id 3 for calling a global function with example usage: df_to_dataset(df, tokenizer, max_seq_length) and returns: features_to_dataset