code: def __init__(self, data_file: str):
      super().__init__(data_file=data_file)
  
      self.tokenizer = CustomTokenizer()
      df = pd.read_csv(data_file)
  
      data_vocab = CBOWVocabulary()
      max_context = 0
      for index, row in df.iterrows():
          tokens = self.tokenizer.tokenize(text=row.context)
          max_context = max(max_context, len(tokens))
          for token in tokens:
              data_vocab.add_token(token)
              data_vocab.add_token(row.target)
      self.data_vocab = data_vocab
      self.target_vocab = data_vocab
      self.max_context = max_context
  
imports: import logging
  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 CBOWVocabulary
  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 CBOWVectorizer object from inherited base classes: Vectorizer with example: obj = CBOWVectorizer(data_file)