code: def _get_model_and_data(
      self,
      batch_shape,
      m,
      outcome_transform=None,
      input_transform=None,
      extra_model_kwargs=None,
      **tkwargs,
  ):
      extra_model_kwargs = extra_model_kwargs or {}
      train_X, train_Y = _get_random_data(
          batch_shape=batch_shape, m=m, **tkwargs
      )
      model_kwargs = {
          "train_X": train_X,
          "train_Y": train_Y,
          "outcome_transform": outcome_transform,
          "input_transform": input_transform,
      }
      model = SingleTaskGP(**model_kwargs, **extra_model_kwargs)
      return model, model_kwargs
  
imports: import itertools
  import warnings
  import torch
  from botorch.exceptions.warnings import OptimizationWarning
  from botorch.fit import fit_gpytorch_mll
  from botorch.models.gp_regression import FixedNoiseGP, HeteroskedasticSingleTaskGP, SingleTaskGP
  from botorch.models.transforms import Normalize, Standardize
  from botorch.models.transforms.input import InputStandardize
  from botorch.posteriors import GPyTorchPosterior
  from botorch.sampling import SobolQMCNormalSampler
  from botorch.utils.datasets import SupervisedDataset
  from botorch.utils.sampling import manual_seed
  from botorch.utils.test_helpers import get_pvar_expected
  from botorch.utils.testing import _get_random_data, BotorchTestCase
  from gpytorch.kernels import MaternKernel, RBFKernel, ScaleKernel
  from gpytorch.likelihoods import _GaussianLikelihoodBase, FixedNoiseGaussianLikelihood, GaussianLikelihood, HeteroskedasticNoise
  from gpytorch.means import ConstantMean, ZeroMean
  from gpytorch.mlls.exact_marginal_log_likelihood import ExactMarginalLogLikelihood
  from gpytorch.mlls.noise_model_added_loss_term import NoiseModelAddedLossTerm
  from gpytorch.priors import GammaPrior
label_desc: Use image node_id 1 for calling the TestSingleTaskGP obj's underlying member method code with example usage: obj._get_model_and_data(batch_shape, m, outcome_transform, input_transform, extra_model_kwargs) and returns: model,  model_kwargs