docstr: Args:
      gp_models: Arbitrary number of GPyTorchModels.
docstr_tok: ["Args",":","gp_models",":","Arbitrary","number","of","GPyTorchModels","."]
code: def __init__(self, *gp_models: GPyTorchModel):
      r"""
      Args:
          gp_models: Arbitrary number of GPyTorchModels.
      """
      super().__init__(*gp_models)
  
imports: import itertools
  import warnings
  from typing import Optional
  import torch
  from botorch import settings
  from botorch.acquisition.objective import ScalarizedPosteriorTransform
  from botorch.exceptions import BotorchTensorDimensionError, BotorchTensorDimensionWarning
  from botorch.exceptions.errors import DeprecationError, InputDataError
  from botorch.fit import fit_gpytorch_mll
  from botorch.models.gpytorch import BatchedMultiOutputGPyTorchModel, GPyTorchModel, ModelListGPyTorchModel
  from botorch.models.model import FantasizeMixin
  from botorch.models.multitask import MultiTaskGP
  from botorch.models.transforms import Standardize
  from botorch.models.transforms.input import ChainedInputTransform, InputTransform
  from botorch.models.utils import fantasize
  from botorch.posteriors.gpytorch import GPyTorchPosterior
  from botorch.sampling.normal import SobolQMCNormalSampler
  from botorch.utils.test_helpers import SimpleGPyTorchModel
  from botorch.utils.testing import _get_random_data, BotorchTestCase
  from gpytorch import ExactMarginalLogLikelihood
  from gpytorch.distributions import MultivariateNormal
  from gpytorch.kernels import RBFKernel, ScaleKernel
  from gpytorch.likelihoods import GaussianLikelihood
  from gpytorch.means import ConstantMean
  from gpytorch.models import ExactGP, IndependentModelList
  from torch import Tensor
label_desc: Use image node_id 1 to create a new SimpleModelListGPyTorchModel object from inherited base classes: IndependentModelList, ModelListGPyTorchModel with example: obj = SimpleModelListGPyTorchModel()