code: def get_doc_frequencies(dtm, min_val=1, proportions=False):
      
      if dtm.ndim != 2:
          raise ValueError('`dtm` must be a 2D array/matrix')
  
      doc_freq = np.sum(dtm >= min_val, axis=0)
  
      if doc_freq.ndim != 1:
          doc_freq = doc_freq.A.flatten()
  
      if proportions:
          return doc_freq / dtm.shape[0]
      else:
          return doc_freq
docs: For each word in the vocab of `dtm` (i.e. its columns), return how often it occurs at least `min_val` times.
      If `proportions` is True, return proportions scaled to the number of documents instead of absolute numbers.