(self, X, y=None)
return self
Do nothing and return the estimator unchanged.

        This method is here to implement the usual scikit-learn API and hence
        work in pipelines.

        Parameters
        ----------
        X : list of length n_samples, or ndarray of shape (n_samples, ...)
            Collection of inputs to be fit-transformed by `transformer`.

        y : None
            There is no need for a target in a transformer, yet the pipeline
            API requires this parameter.

        Returns
        -------
        self : object
Do nothing and return the estimator unchanged.
Do nothing and return the estimator unchanged .
def fit(self, X, y=None):
        """Do nothing and return the estimator unchanged.

        This method is here to implement the usual scikit-learn API and hence
        work in pipelines.

        Parameters
        ----------
        X : list of length n_samples, or ndarray of shape (n_samples, ...)
            Collection of inputs to be fit-transformed by `transformer`.

        y : None
            There is no need for a target in a transformer, yet the pipeline
            API requires this parameter.

        Returns
        -------
        self : object

        """
        check_collection(X, accept_sparse=True, accept_large_sparse=True,
                         force_all_finite=False)
        self._validate_transformer()

        self._is_fitted = True
        return self
def fit ( self , X , y = None ) : check_collection ( X , accept_sparse = True , accept_large_sparse = True , force_all_finite = False ) self . _validate_transformer ( ) self . _is_fitted = True return self