class_docstr: The Multi-Task GP with the latent context embedding multioutput (LCE-M)
  kernel. See [Feng2020HDCPS]_ for a reference on the model and its use in Bayesian
  optimization.
code: def forward(self, x: Tensor) -> MultivariateNormal:
      if self.training:
          x = self.transform_inputs(x)
      x_basic, task_idcs = self._split_inputs(x)
      # Compute base mean and covariance
      mean_x = self.mean_module(x_basic)
      covar_x = self.covar_module(x_basic)
      # Compute task covariances
      covar_i = self.task_covar_matrix(task_idcs)
      covar = covar_x.mul(covar_i)
      return MultivariateNormal(mean_x, covar)
  
imports: import warnings
  from typing import List, Optional
  import torch
  from botorch.models.multitask import MultiTaskGP
  from botorch.models.transforms.input import InputTransform
  from botorch.models.transforms.outcome import OutcomeTransform
  from gpytorch.constraints import Interval
  from gpytorch.distributions.multivariate_normal import MultivariateNormal
  from gpytorch.kernels.rbf_kernel import RBFKernel
  from linear_operator.operators import InterpolatedLinearOperator, LinearOperator
  from torch import Tensor
  from torch.nn import ModuleList
label_desc: Use image node_id 5 for calling the LCEMGP obj's underlying member method code with example usage: obj.forward(x) and returns: MultivariateNormal