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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 transform(self, X, y=None, copy=None):
 """ Perform standardization by centering and scaling using the parameters. :param X: Data matrix to scale. :type X: numpy.ndarray, shape [n_samples, n_features] :param y: Passthrough for scikit-learn ``Pipeline`` compatibility. :type y: None :param bool copy: Copy the X matrix. :return: Scaled version of the X data matrix. :rtype: numpy.ndarray, shape [n_samples, n_features] """

        check_is_fitted(self, 'scale_')

        copy = copy if copy is not None else self.copy

        X = check_array(X, accept_sparse='csr', copy=copy, warn_on_dtype=True,
                        estimator=self, dtype=FLOAT_DTYPES)

        if sparse.issparse(X):
            if self.with_mean:
                raise ValueError(
                    "Cannot center sparse matrices: pass `with_mean=False` "
                    "instead. See docstring for motivation and alternatives.")
            if self.scale_ is not None:
                inplace_column_scale(X, 1 / self.scale_)
        else:
            if self.with_mean:
                X -= self.mean_
            if self.with_std:
                X /= self.scale_
        return X