code: def gradients_X(self, dL_dK, X, X2=None):
          
  
          target = np.zeros(X.shape)
          [target.__iadd__(p.gradients_X(dL_dK, X, X2)) for p in self.parts]
          return target
docs: Compute the gradient of the objective function with respect to X.
  
          :param dL_dK: An array of gradients of the objective function with respect to the covariance function.
          :type dL_dK: np.ndarray (num_samples x num_inducing)
          :param X: Observed data inputs
          :type X: np.ndarray (num_samples x input_dim)
          :param X2: Observed data inputs (optional, defaults to X)
          :type X2: np.ndarray (num_inducing x input_dim)