code: def subset_state_dict(
      state_dict: OrderedDict[str, Tensor],
      submodel_index: int,
  ) -> OrderedDict[str, Tensor]:
      """Get the state dict for a submodel from the state dict of a model list.
  
      Args:
          state_dict: A state dict.
          submodel_index: The index of the submodel to extract.
  
      Returns:
          The state dict for the submodel.
      """
      expected_substring = f"models.{submodel_index}."
      len_substring = len(expected_substring)
      new_items = [
          (k[len_substring:], v)
          for k, v in state_dict.items()
          if k.startswith(expected_substring)
      ]
      return OrderedDict(new_items)
  
imports: import warnings
  from logging import Logger
  from typing import Any, Callable, Dict, List, Optional, OrderedDict, Tuple, Type
  import torch
  from ax.core.search_space import SearchSpaceDigest
  from ax.exceptions.core import AxError, AxWarning, UnsupportedError
  from ax.models.types import TConfig
  from ax.utils.common.constants import Keys
  from ax.utils.common.logger import get_logger
  from ax.utils.common.typeutils import checked_cast, not_none
  from botorch.acquisition.acquisition import AcquisitionFunction
  from botorch.acquisition.logei import qLogNoisyExpectedImprovement
  from botorch.acquisition.multi_objective.logei import qLogNoisyExpectedHypervolumeImprovement
  from botorch.fit import fit_fully_bayesian_model_nuts, fit_gpytorch_mll
  from botorch.models.fully_bayesian import SaasFullyBayesianSingleTaskGP
  from botorch.models.gp_regression import SingleTaskGP
  from botorch.models.gp_regression_fidelity import SingleTaskMultiFidelityGP
  from botorch.models.gp_regression_mixed import MixedSingleTaskGP
  from botorch.models.gpytorch import BatchedMultiOutputGPyTorchModel, GPyTorchModel
  from botorch.models.model import Model, ModelList
  from botorch.models.multitask import MultiTaskGP
  from botorch.models.pairwise_gp import PairwiseGP
  from botorch.utils.datasets import SupervisedDataset
  from botorch.utils.transforms import is_fully_bayesian
  from gpytorch.mlls.marginal_log_likelihood import MarginalLogLikelihood
  from torch import Tensor
label_desc: Use image node_id 12 for calling a global function with example usage: subset_state_dict(state_dict, submodel_index) and returns: OrderedDict