code: def transform(self, X, y=None, copy=None):
          
          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
docs: 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]