code: def __init__(self, train_x, train_y, likelihood):
      super(MultitaskGPModel, self).__init__(
          train_x, train_y, likelihood
      )
      self.mean_module = MultitaskMean(ConstantMean(), num_tasks=2)
      self.covar_module = gpytorch.kernels.MultitaskKernel(
          gpytorch.kernels.InducingPointKernel(
              gpytorch.kernels.RBFKernel(),
              inducing_points=torch.randn(50, 1),
              likelihood=likelihood,
          ),
          num_tasks=2,
          rank=2,
      )
  
file_path: gpytorch/test/examples/test_kronecker_multitask_sgpr_regression.py
imports: import unittest
  from math import pi
  import torch
  import gpytorch
  from gpytorch.distributions import MultitaskMultivariateNormal
  from gpytorch.likelihoods import MultitaskGaussianLikelihood
  from gpytorch.means import ConstantMean, MultitaskMean
  from gpytorch.test.base_test_case import BaseTestCase
label_desc: Use image node_id 1 to create a new MultitaskGPModel object from inherited base classes: gpytorch.models with example: obj = MultitaskGPModel(train_x, train_y, likelihood)