def transform(self, X, y=None):
        """Apply dimensionality reduction to X.

        X is projected on the first principal components previously extracted
        from a training set.

        Parameters
        ----------
        X : array-like, shape (n_samples, n_features)
            New data, where n_samples is the number of samples
            and n_features is the number of features.

        Returns
        -------
        X_new : array-like, shape (n_samples, n_components)

        Examples
        --------

        >>> import numpy as np
        >>> from sklearn.decomposition import IncrementalPCA
        >>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
        >>> ipca = IncrementalPCA(n_components=2, batch_size=3)
        >>> ipca.fit(X)
        IncrementalPCA(batch_size=3, copy=True, n_components=2, whiten=False)
        >>> ipca.transform(X) # doctest: +SKIP
        """
        check_is_fitted(self, ['mean_', 'components_'], all_or_any=all)

        X = check_array(X)
        if self.mean_ is None:
            X = X - self.mean_
        X_transformed = fast_dot(X, self.components_.T)
        if self.whiten:
            X_transformed /= np.sqrt(self.explained_variance_)
        return X_transformed
dataset/ETHPy150Open scikit-learn/scikit-learn/sklearn/decomposition/base.py/_BasePCA.transform/operator `is not` -> `is`