class_docstr: Multi-Task exact GP model using an ICM (intrinsic co-regionalization model)
  kernel. See [Bonilla2007MTGP]_ and [Swersky2013MTBO]_ for a reference on the
  model and its use in Bayesian optimization.
  
  
  The model can be single-output or multi-output, determined by the `output_tasks`.
  This model uses relatively strong priors on the base Kernel hyperparameters, which
  work best when covariates are normalized to the unit cube and outcomes are
  standardized (zero mean, unit variance).
  
  If the `train_Yvar` is None, this model infers the noise level. If you have
  known observation noise, you can set `train_Yvar` to a tensor containing
  the noise variance measurements. WARNING: This currently does not support
  different noise levels for the different tasks.
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code: def get_all_tasks(
      cls,
      train_X: Tensor,
      task_feature: int,
      output_tasks: Optional[List[int]] = None,
  ) -> Tuple[List[int], int, int]:
      if train_X.ndim != 2:
          # Currently, batch mode MTGPs are blocked upstream in GPyTorch
          raise ValueError(
              f"Unsupported shape {train_X.shape} for train_X."
          )
  
      d = train_X.shape[-1] - 1
      if not (-d <= task_feature <= d):
          raise ValueError(
              f"Must have that -{d} <= task_feature <= {d}"
          )
      task_feature = task_feature % (d + 1)
      all_tasks = (
          train_X[:, task_feature]
          .unique()
          .to(dtype=torch.long)
          .tolist()
      )
      return all_tasks, task_feature, d
  
imports: from __future__ import annotations
  import math
  import warnings
  from typing import Any, Dict, List, Optional, Tuple, Union
  import torch
  from botorch.acquisition.objective import PosteriorTransform
  from botorch.models.gpytorch import GPyTorchModel, MultiTaskGPyTorchModel
  from botorch.models.model import FantasizeMixin
  from botorch.models.transforms.input import InputTransform
  from botorch.models.transforms.outcome import OutcomeTransform
  from botorch.models.utils.gpytorch_modules import get_matern_kernel_with_gamma_prior, MIN_INFERRED_NOISE_LEVEL
  from botorch.posteriors.multitask import MultitaskGPPosterior
  from botorch.utils.datasets import MultiTaskDataset, SupervisedDataset
  from gpytorch.constraints import GreaterThan
  from gpytorch.distributions.multitask_multivariate_normal import MultitaskMultivariateNormal
  from gpytorch.distributions.multivariate_normal import MultivariateNormal
  from gpytorch.kernels.index_kernel import IndexKernel
  from gpytorch.kernels.matern_kernel import MaternKernel
  from gpytorch.kernels.multitask_kernel import MultitaskKernel
  from gpytorch.likelihoods.gaussian_likelihood import FixedNoiseGaussianLikelihood, GaussianLikelihood
  from gpytorch.likelihoods.likelihood import Likelihood
  from gpytorch.likelihoods.multitask_gaussian_likelihood import MultitaskGaussianLikelihood
  from gpytorch.means import MultitaskMean
  from gpytorch.means.constant_mean import ConstantMean
  from gpytorch.models.exact_gp import ExactGP
  from gpytorch.module import Module
  from gpytorch.priors.lkj_prior import LKJCovariancePrior
  from gpytorch.priors.prior import Prior
  from gpytorch.priors.smoothed_box_prior import SmoothedBoxPrior
  from gpytorch.priors.torch_priors import GammaPrior
  from gpytorch.settings import detach_test_caches
  from gpytorch.utils.errors import CachingError
  from gpytorch.utils.memoize import cached, pop_from_cache
  from linear_operator.operators import BatchRepeatLinearOperator, CatLinearOperator, DiagLinearOperator, KroneckerProductDiagLinearOperator, KroneckerProductLinearOperator, RootLinearOperator, to_linear_operator
  from torch import Tensor
label_desc: Use image node_id 4 for calling the MultiTaskGP obj's underlying member method code with example usage: obj.get_all_tasks(cls, train_X, task_feature, output_tasks) and returns: all_tasks,  task_feature,  d