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: compute covariance matrix of a list of given context
  
  Args:
      task_idcs: (n x 1) or (b x n x 1) task indices tensor
docstr_tok: ["compute","covariance","matrix","of","a","list","of","given","context","Args",":","task_idcs",":","(","n","x","1",")","or","(","b","x","n","x","1",")","task","indices","tensor"]
code: def task_covar_matrix(self, task_idcs: Tensor) -> Tensor:
      r"""compute covariance matrix of a list of given context
  
      Args:
          task_idcs: (n x 1) or (b x n x 1) task indices tensor
  
      """
      covar_matrix = self._eval_context_covar()
      return InterpolatedLinearOperator(
          base_linear_op=covar_matrix,
          left_interp_indices=task_idcs,
          right_interp_indices=task_idcs,
      ).to_dense()
  
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 4 for calling the LCEMGP obj's underlying member method code with example usage: obj.task_covar_matrix(task_idcs) and returns: InterpolatedLinearOperator