code: def vectorize(self, context: str) -> np.array:
      tokens = self.tokenizer.tokenize(text=context)
      indices = [
          self.data_vocab.lookup_token(token) for token in tokens
      ]
      vector_length = self.max_context
  
      out_vector = np.zeros(vector_length, dtype=np.int64)
      out_vector[: len(indices)] = indices
      out_vector[len(indices) :] = self.data_vocab.mask_index
  
      return out_vector
  
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 2 for calling the CBOWVectorizer obj's underlying member method code with example usage: obj.vectorize(context) and returns: out_vector