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.
docstr: obtain context covariance matrix (num_contexts x num_contexts)
docstr_tok: ["obtain","context","covariance","matrix","(","num_contexts","x","num_contexts",")"]
code: def _eval_context_covar(self) -> LinearOperator:
      """obtain context covariance matrix (num_contexts x num_contexts)"""
      all_embs = self._task_embeddings()
      return self.task_covar_module(all_embs)
  
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 2 for calling the LCEMGP obj's underlying member method code with example usage: obj._eval_context_covar() and returns: self