code: def forward(self, x, i):
      # Get predictive mean
      mean_x = self.mean_module(x)
      # Get all covariances, we'll look up the task-speicific ones
      covar_x = self.covar_module(x)
      # # Get the covariance for task i
      covar_i = self.task_covar_module(i)
      covar_xi = covar_x.mul(covar_i)
      return MultivariateNormal(mean_x, covar_xi)
  
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 2 for calling the HadamardMultitaskGPModel obj's underlying member method code with example usage: obj.forward(x, i) and returns: MultivariateNormal