code: def transform(self, X, lenscale=None):
          
          N, D = X.shape
          lenscale = self._check_dim(D, lenscale)[:, np.newaxis]
  
          WX = np.dot(X, self.W / lenscale)
  
          return np.hstack((np.cos(WX), np.sin(WX))) / np.sqrt(self.n)
docs: Apply the random basis to X.
  
          Parameters
          ----------
          X: ndarray
              (N, d) array of observations where N is the number of samples, and
              d is the dimensionality of X.
          lenscale: scalar or ndarray, optional
              scalar or array of shape (d,) length scales (one for each dimension
              of X). If not input, this uses the value of the initial length
              scale.
  
          Returns
          -------
          ndarray:
              of shape (N, 2*nbases) where nbases is number of random bases to
              use, given in the constructor.