code: def __init__(self, train_X, train_Y):
      likelihood = MultitaskGaussianLikelihood(num_tasks=1 + dim)
      super().__init__(train_X, train_Y, likelihood)
      self.mean_module = ConstantMeanGrad()
      self.base_kernel = RBFKernelGrad()
      self.covar_module = ScaleKernel(self.base_kernel)
      self._num_outputs = 1 + dim
  
file_path: gpytorch/test/examples/test_derivative_gp_fantasy.py
imports: import unittest
  from math import pi
  import torch
  import gpytorch
  from gpytorch.distributions import MultitaskMultivariateNormal
  from gpytorch.kernels import ScaleKernel, RBFKernelGrad
  from gpytorch.likelihoods import MultitaskGaussianLikelihood
  from gpytorch.means import ConstantMeanGrad
  from gpytorch.test.base_test_case import BaseTestCase
label_desc: Use image node_id 1 to create a new GPWithDerivatives object from inherited base classes: gpytorch.models with example: obj = GPWithDerivatives(train_X, train_Y)