code: def test_transform_revert_train_inputs(self):
      tkwargs = {"device": self.device, "dtype": torch.float}
      model_list = _get_model(use_intf=True, **tkwargs)
      org_inputs = [m.train_inputs[0] for m in model_list.models]
      model_list.eval()
      for i, m in enumerate(model_list.models):
          self.assertTrue(
              torch.allclose(
                  m.train_inputs[0],
                  m.input_transform.preprocess_transform(org_inputs[i]),
              )
          )
          self.assertTrue(m._has_transformed_inputs)
          self.assertTrue(
              torch.equal(m._original_train_inputs, org_inputs[i])
          )
      model_list.train(mode=True)
      for i, m in enumerate(model_list.models):
          self.assertTrue(torch.equal(m.train_inputs[0], org_inputs[i]))
          self.assertFalse(m._has_transformed_inputs)
      model_list.train(mode=False)
      for i, m in enumerate(model_list.models):
          self.assertTrue(
              torch.allclose(
                  m.train_inputs[0],
                  m.input_transform.preprocess_transform(org_inputs[i]),
              )
          )
          self.assertTrue(m._has_transformed_inputs)
          self.assertTrue(
              torch.equal(m._original_train_inputs, org_inputs[i])
          )
  
imports: import itertools
  import warnings
  from copy import deepcopy
  from typing import Optional
  import torch
  from botorch.acquisition.objective import ScalarizedPosteriorTransform
  from botorch.exceptions.errors import BotorchTensorDimensionError
  from botorch.exceptions.warnings import OptimizationWarning
  from botorch.fit import fit_gpytorch_mll
  from botorch.models.gp_regression import SingleTaskGP
  from botorch.models.model_list_gp_regression import ModelListGP
  from botorch.models.multitask import MultiTaskGP
  from botorch.models.transforms.input import Normalize
  from botorch.models.transforms.outcome import ChainedOutcomeTransform, Log, Standardize
  from botorch.posteriors import GPyTorchPosterior, PosteriorList, TransformedPosterior
  from botorch.sampling.base import MCSampler
  from botorch.sampling.list_sampler import ListSampler
  from botorch.sampling.normal import IIDNormalSampler
  from botorch.utils.testing import _get_random_data, BotorchTestCase
  from gpytorch.distributions import MultitaskMultivariateNormal, MultivariateNormal
  from gpytorch.kernels import MaternKernel, ScaleKernel
  from gpytorch.likelihoods import LikelihoodList
  from gpytorch.likelihoods.gaussian_likelihood import FixedNoiseGaussianLikelihood, GaussianLikelihood
  from gpytorch.means import ConstantMean
  from gpytorch.mlls import SumMarginalLogLikelihood
  from gpytorch.mlls.exact_marginal_log_likelihood import ExactMarginalLogLikelihood
  from gpytorch.priors import GammaPrior
  from torch import Tensor
label_desc: Use image node_id 6 for calling the TestModelListGP obj's underlying member method code with example usage: obj.test_transform_revert_train_inputs() without return types