class_docstr: Loads a pre-weighted inverted index of token/document terms.
  
  Scores new queries by taking sparse dot products.
docstr: Parse the query into tokens (either ngrams or tokens).
code: def parse(self, query):
      """
      Parse the query into tokens (either ngrams or tokens).
      """
      tokens = self.tokenizer.tokenize(query)
      return tokens.ngrams(
          n=self.ngrams, uncased=True, filter_fn=utils.filter_ngram
      )
  
file_path: ParlAI/parlai/agents/tfidf_retriever/tfidf_doc_ranker.py
imports: import numpy
  import scipy.sparse
  from .None import utils
  from .None import tokenizers
  from parlai.utils.logging import logger
label_desc: Use image node_id 4 for calling the TfidfDocRanker obj's underlying member method code with example usage: obj.parse(query) and returns: tokens