code: def __init__(self, data_file: str, cutoff: int):
      super().__init__(data_file=data_file)
      self.cutoff = cutoff
  
      self.tokenizer = CustomTokenizer()
      df = pd.read_csv(data_file)
  
      target_vocab = Vocabulary(add_unk=False)
      for category in sorted(set(df.category)):
          target_vocab.add_token(category)
  
      word_counts = Counter()
      max_title = 0
      for title in df.title:
          tokens = self.tokenizer.tokenize(text=title)
          max_title = max(max_title, len(tokens))
          for token in tokens:
              if token not in string.punctuation:
                  word_counts[token] += 1
  
      data_vocab = SequenceVocabulary()
      for word, word_count in word_counts.items():
          if word_count >= self.cutoff:
              data_vocab.add_token(word)
  
      self.data_vocab = data_vocab
      self.target_vocab = target_vocab
      self.max_title = max_title + 2
  
imports: import logging
  import string
  from collections import Counter
  from typing import Dict, List, Any
  import numpy
  import pandas
  import torch
  from transfer_nlp.common.tokenizers import CustomTokenizer
  from transfer_nlp.embeddings.embeddings import Embedding
  from transfer_nlp.loaders.loaders import DatasetSplits, DataFrameDataset
  from transfer_nlp.loaders.vectorizers import Vectorizer
  from transfer_nlp.loaders.vocabulary import Vocabulary, SequenceVocabulary
  from transfer_nlp.plugins.config import register_plugin
  from transfer_nlp.plugins.predictors import PredictorABC
label_desc: Use image node_id 1 to create a new NewsVectorizer object from inherited base classes: Vectorizer with example: obj = NewsVectorizer(data_file, cutoff)