(self, tokenized_docs: Iterable[Iterable[str]])
return doc_term_matrix
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.
        Transform ``tokenized_docs`` into a document-term matrix with values
        weighted according to the parameters in :class:`Vectorizer` initialization.

        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:
            The transformed document-term matrix, where rows correspond to documents
            and columns correspond to terms, as a sparse row matrix.
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.
        Transform ``tokenized_docs`` into a document-term matrix with values
        weighted according to the parameters in :class:`Vectorizer` initialization.
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 . Transform tokenized_docs into a document - term matrix with values weighted according to the parameters in : class : Vectorizer initialization .
def fit_transform(self, tokenized_docs: Iterable[Iterable[str]]) -> sp.csr_matrix:
        """
        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.
        Transform ``tokenized_docs`` into a document-term matrix with values
        weighted according to the parameters in :class:`Vectorizer` initialization.

        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:
            The transformed document-term matrix, where rows correspond to documents
            and columns correspond to terms, as a sparse row matrix.
        """
        # count terms and fit global weights
        doc_term_matrix = self._fit(tokenized_docs)
        # re-weight values in doc-term matrix, as specified in init
        doc_term_matrix = self._reweight_values(doc_term_matrix)
        return doc_term_matrix
def fit_transform ( self , tokenized_docs : Iterable [ Iterable [ str ] ] ) -> sp . csr_matrix : # count terms and fit global weights doc_term_matrix = self . _fit ( tokenized_docs ) # re-weight values in doc-term matrix, as specified in init doc_term_matrix = self . _reweight_values ( doc_term_matrix ) return doc_term_matrix