code: def __init__(self, train_x, train_y, likelihood):
      super(HadamardMultitaskGPModel, self).__init__(
          train_x, train_y, likelihood
      )
      # Default bounds on mean are (-1e10, 1e10)
      self.mean_module = ConstantMean()
      # We use the very common RBF kernel
      self.covar_module = RBFKernel()
      # We learn an IndexKernel for 2 tasks
      # (so we'll actually learn 2x2=4 tasks with correlations)
      sd_prior = SmoothedBoxPrior(exp(-4), exp(4))
      cov_prior = LKJCovariancePrior(n=2, eta=1, sd_prior=sd_prior)
      self.task_covar_module = IndexKernel(
          num_tasks=2, rank=1, prior=cov_prior
      )
  
imports: import os
  import random
  import unittest
  from math import exp, pi
  import torch
  from torch import optim
  import gpytorch
  from gpytorch.distributions import MultivariateNormal
  from gpytorch.kernels import IndexKernel, RBFKernel
  from gpytorch.likelihoods import GaussianLikelihood
  from gpytorch.means import ConstantMean
  from gpytorch.priors import LKJCovariancePrior, SmoothedBoxPrior
label_desc: Use image node_id 1 to create a new HadamardMultitaskGPModel object from inherited base classes: gpytorch.models with example: obj = HadamardMultitaskGPModel(train_x, train_y, likelihood)