docs: def tag_text(self, text, **kwargs):
          """Annotates `analysis` entries in `corpus` with a list of lemmas` synsets and queried WordNet data in a 'wordnet' entry.
  
          Note
          ----
            Annotates every `analysis` entry with a `wordnet`:{`synsets`:[..]}.
  
          Parameters
          ----------
          text: estnltk.text.Text
            Representation of a corpus in a disassembled form for automatic text analysis with word-level `analysis` entry.
            E.g. corpus disassembled into paragraphs, sentences, words ({'paragraphs':[{'sentences':[{'words':[{'analysis':{...}},..]},..]},..]}).
          pos : boolean, optional
            If True, annotates each synset with a correspnding `pos` (part-of-speech) tag.
          variants : boolean, optional
            If True, annotates each synset with a list of all its variants' (lemmas') literals.
          var_sense : boolean, optional
            If True and `variants` is True, annotates each variant/lemma with its sense number.
          var_definition : boolean, optional
            If True and `variants` is True, annotates each variant/lemma with its definition. Definitions often missing in WordNet.
          var_examples : boolean, optional
            If True and `variants` is True, annotates each variant/lemma with a list of its examples. Examples often missing in WordNet.
          relations : list of str, optional
            Holds interested relations. Legal relations are as follows:
          `antonym`, `be_in_state`, `belongs_to_class`, `causes`, `fuzzynym`, `has_holo_location`, `has_holo_madeof`, `has_holo_member`,
          `has_holo_part`, `has_holo_portion`, `has_holonym`, `has_hyperonym`, `has_hyponym`, `has_instance`, `has_mero_location`,
          `has_mero_madeof`, `has_mero_member`, `has_mero_part`, `has_mero_portion`, `has_meronym`, `has_subevent`, `has_xpos_hyperonym`,
          `has_xpos_hyponym`, `involved`, `involved_agent`, `involved_instrument`, `involved_location`, `involved_patient`,
          `involved_target_direction`, `is_caused_by`, `is_subevent_of`, `near_antonym`, `near_synonym`, `role`, `role_agent`, `role_instrument`,
          `role_location`, `role_patient`, `role_target_direction`, `state_of`, `xpos_fuzzynym`, `xpos_near_antonym`, `xpos_near_synonym`.
            Annotates each synset with related synsets' indices with respect to queried relations.
  
          Returns
          -------
          estnltk.text.Text
            In-place annotated `text`.
  
          """
  
          for analysis_match in text.analysis:
              for candidate in analysis_match:
                  if candidate['partofspeech'] in PYVABAMORF_TO_WORDNET_POS_MAP:
                      # Wordnet contains data about the given lemma and pos combination - will annotate.
                      wordnet_obj = {}
                      tag_synsets(wordnet_obj, candidate, **kwargs)
          return text