original_docstring: 
          Calculate the gradient of the prediction at Xnew w.r.t Xnew.
          
docstring: Calculate the gradient of the prediction at Xnew w.r.t Xnew.
docstring_tokens: Calculate the gradient of the prediction at Xnew w . r . t Xnew .
code: def dmu_dX(self, Xnew):
          """
          Calculate the gradient of the prediction at Xnew w.r.t Xnew.
          """
          dmu_dX = np.zeros_like(Xnew)
          for i in range(self.Z.shape[0]):
              dmu_dX += self.kern.gradients_X(self.grad_dict['dL_dpsi1'][i:i + 1, :], Xnew, self.Z[i:i + 1, :])
          return dmu_dX
code_tokens: def dmu_dX ( self , Xnew ) : dmu_dX = np . zeros_like ( Xnew ) for i in range ( self . Z . shape [ 0 ] ) : dmu_dX += self . kern . gradients_X ( self . grad_dict [ 'dL_dpsi1' ] [ i : i + 1 , : ] , Xnew , self . Z [ i : i + 1 , : ] ) return dmu_dX
short_docstring: Calculate the gradient of the prediction at Xnew w.r.t Xnew.
short_docstring_tokens: Calculate the gradient of the prediction at Xnew w . r . t Xnew .
comment: """
          Calculate the gradient of the prediction at Xnew w.r.t Xnew.
          """