code: def _make_rand_mini_data(
      self,
      batch_shape,
      X_dim=2,
  ) -> Tuple[Tensor, Tensor]:
      train_X = torch.rand(
          *batch_shape, 2, X_dim, device=self.device, dtype=self.dtype
      )
      train_Y = train_X.sum(dim=-1, keepdim=True)
      train_comp = torch.topk(train_Y, k=2, dim=-2).indices.transpose(
          -1, -2
      )
  
      return train_X, train_comp
  
imports: import itertools
  import random
  import warnings
  from typing import Dict, Tuple, Union
  import torch
  from botorch.acquisition.objective import ScalarizedPosteriorTransform
  from botorch.exceptions import OptimizationWarning, UnsupportedError
  from botorch.exceptions.warnings import _get_single_precision_warning, InputDataWarning
  from botorch.fit import fit_gpytorch_mll
  from botorch.models.likelihoods.pairwise import PairwiseLikelihood, PairwiseLogitLikelihood, PairwiseProbitLikelihood
  from botorch.models.model import Model
  from botorch.models.pairwise_gp import _ensure_psd_with_jitter, PairwiseGP, PairwiseLaplaceMarginalLogLikelihood
  from botorch.models.transforms.input import Normalize
  from botorch.posteriors import GPyTorchPosterior
  from botorch.sampling.pairwise_samplers import PairwiseSobolQMCNormalSampler
  from botorch.utils.testing import BotorchTestCase
  from gpytorch.kernels import RBFKernel, ScaleKernel
  from gpytorch.kernels.linear_kernel import LinearKernel
  from gpytorch.means import ConstantMean
  from gpytorch.priors import GammaPrior, SmoothedBoxPrior
  from linear_operator.utils.errors import NotPSDError
  from torch import Tensor
label_desc: Use image node_id 2 for calling the TestPairwiseGP obj's underlying member method code with example usage: obj._make_rand_mini_data(batch_shape, X_dim) and returns: train_X,  train_comp