parameters: (self, raw_documents, unused_y=None)
return_statement: return self
docstring: Learn a vocabulary dictionary of all tokens in the raw documents.
  
      Args:
        raw_documents: An iterable which yield either str or unicode.
        unused_y: to match fit format signature of estimators.
  
      Returns:
        self
docstring_summary: Learn a vocabulary dictionary of all tokens in the raw documents.
docstring_tokens: Learn a vocabulary dictionary of all tokens in the raw documents .
function: def fit(self, raw_documents, unused_y=None):
      """Learn a vocabulary dictionary of all tokens in the raw documents.
  
      Args:
        raw_documents: An iterable which yield either str or unicode.
        unused_y: to match fit format signature of estimators.
  
      Returns:
        self
      """
      for tokens in self._tokenizer(raw_documents):
        for token in tokens:
          self.vocabulary_.add(token)
      if self.min_frequency > 0:
        self.vocabulary_.trim(self.min_frequency)
      self.vocabulary_.freeze()
      return self
function_tokens: def fit ( self , raw_documents , unused_y = None ) : for tokens in self . _tokenizer ( raw_documents ) : for token in tokens : self . vocabulary_ . add ( token ) if self . min_frequency > 0 : self . vocabulary_ . trim ( self . min_frequency ) self . vocabulary_ . freeze ( ) return self