class_docstr: Objects of this abstract class realize the transformation between word-document co-occurrence
  matrix (int) into a BM25 matrix (positive floats). Concrete subclasses of this abstract class
  implement different BM25 scoring functions.
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docstr: Compute vector space weights for a set of terms in a document.
  
  Parameters
  ----------
  num_tokens : int
      The number of tokens in the document.
  term_frequencies : ndarray
      1D array of term frequencies.
  idfs : ndarray
      1D array of inverse term document frequencies.
  
  Returns
  -------
  term_weights : ndarray
      1D array of vector space weights.
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code: def get_term_weights(self, num_tokens, term_frequencies, idfs):
      """Compute vector space weights for a set of terms in a document.
  
      Parameters
      ----------
      num_tokens : int
          The number of tokens in the document.
      term_frequencies : ndarray
          1D array of term frequencies.
      idfs : ndarray
          1D array of inverse term document frequencies.
  
      Returns
      -------
      term_weights : ndarray
          1D array of vector space weights.
  
      """
      pass
  
imports: from abc import ABCMeta, abstractmethod
  from collections import defaultdict
  import logging
  import math
  from gensim import interfaces, utils
  import numpy
label_desc: Use image node_id 3 for calling the BM25ABC obj's underlying member method code with example usage: obj.get_term_weights(num_tokens, term_frequencies, idfs) without return types