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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 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. 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