class_docstr: Ranks terms by their document-length adjusted frequency instead of their raw frequency.
  This means that each term has a document-specific weight of  #(t,d)/|d|.
code: def get_ranks(self, label_append: str = " freq") -> pd.DataFrame:
      X = self.get_term_doc_mat()
      y = self._corpus.get_category_ids()
      doc_lengths = X.sum(axis=1)
      norm_x = np.nan_to_num(X / doc_lengths, 0)
      data = {}
      for i in set(y):
          cat = self._corpus.get_category_index_store().getval(i)
          data[cat + label_append] = norm_x[y == i, :].sum(axis=0).A1
      return pd.DataFrame(
          data,
          index=self._corpus.get_terms(
              use_metadata=self._use_non_text_features
          ),
      )
  
imports: import numpy
  import pandas
  from scipy.sparse import csr_matrix
  from scattertext.termranking.TermRanker import TermRanker
label_desc: Use image node_id 1 for calling the DocLengthNormalizedFrequencyRanker obj's underlying member method code with example usage: obj.get_ranks(label_append) and returns: pd