docs: def count(self, X):
          """
          Called from the fit method, this method gets all the
          words from the corpus and their corresponding frequency
          counts.
  
          Parameters
          ----------
  
          X : ndarray or masked ndarray
              Pass in the matrix of vectorized documents, can be masked in
              order to sum the word frequencies for only a subset of documents.
  
          Returns
          -------
  
          counts : array
              A vector containing the counts of all words in X (columns)
  
          """
          # Sum on axis 0 (by columns), each column is a word
          # Convert the matrix to an array
          # Squeeze to remove the 1 dimension objects (like ravel)
          return np.squeeze(np.asarray(X.sum(axis=0)))