(self, tokenized_docs: Iterable[Iterable[str]])
return self
Count terms in ``tokenized_docs`` and, if not already provided, build up
        a vocabulary based those terms. Fit and store global weights (IDFs)
        and, if needed for term weighting, the average document length.

        Args:
            tokenized_docs: A sequence of tokenized documents, where each is
                a sequence of term strings. For example::

                    >>> ([tok.lemma_ for tok in spacy_doc]
                    ...  for spacy_doc in spacy_docs)
                    >>> ((ne.text for ne in extract.entities(doc))
                    ...  for doc in corpus)

        Returns:
            Vectorizer instance that has just been fit.
Count terms in ``tokenized_docs`` and, if not already provided, build up
        a vocabulary based those terms. Fit and store global weights (IDFs)
        and, if needed for term weighting, the average document length.
Count terms in tokenized_docs and if not already provided build up a vocabulary based those terms . Fit and store global weights ( IDFs ) and if needed for term weighting the average document length .
def fit(self, tokenized_docs: Iterable[Iterable[str]]) -> "Vectorizer":
        """
        Count terms in ``tokenized_docs`` and, if not already provided, build up
        a vocabulary based those terms. Fit and store global weights (IDFs)
        and, if needed for term weighting, the average document length.

        Args:
            tokenized_docs: A sequence of tokenized documents, where each is
                a sequence of term strings. For example::

                    >>> ([tok.lemma_ for tok in spacy_doc]
                    ...  for spacy_doc in spacy_docs)
                    >>> ((ne.text for ne in extract.entities(doc))
                    ...  for doc in corpus)

        Returns:
            Vectorizer instance that has just been fit.
        """
        _ = self._fit(tokenized_docs)
        return self
def fit ( self , tokenized_docs : Iterable [ Iterable [ str ] ] ) -> "Vectorizer" : _ = self . _fit ( tokenized_docs ) return self