code: def get_model(
      dtype,
      device,
      num_points,
      input_dim,
      num_objectives,
      use_model_list,
      standardize_model,
  ):
      torch.manual_seed(123)
      tkwargs = {"dtype": dtype, "device": device}
      train_X = torch.rand(num_points, input_dim, **tkwargs)
      train_Y = torch.rand(num_points, num_objectives, **tkwargs)
  
      if standardize_model:
          if use_model_list:
              outcome_transform = Standardize(m=1)
          else:
              outcome_transform = Standardize(m=num_objectives)
      else:
          outcome_transform = None
  
      if use_model_list and num_objectives > 1:
          model = ModelListGP(
              *[
                  SingleTaskGP(
                      train_X=train_X,
                      train_Y=train_Y[:, i : i + 1],
                      outcome_transform=outcome_transform,
                  )
                  for i in range(num_objectives)
              ]
          )
      else:
          model = SingleTaskGP(
              train_X=train_X,
              train_Y=train_Y,
              outcome_transform=outcome_transform,
          )
  
      return model.eval(), train_X, train_Y
  
imports: import warnings
  from itertools import product
  from unittest import mock
  import torch
  from botorch.acquisition.multi_objective.objective import MCMultiOutputObjective, UnstandardizeMCMultiOutputObjective
  from botorch.acquisition.multi_objective.utils import compute_sample_box_decomposition, get_default_partitioning_alpha, prune_inferior_points_multi_objective, random_search_optimizer, sample_optimal_points
  from botorch.exceptions.errors import UnsupportedError
  from botorch.models.gp_regression import SingleTaskGP
  from botorch.models.model_list_gp_regression import ModelListGP
  from botorch.models.transforms.outcome import Standardize
  from botorch.utils.gp_sampling import get_gp_samples
  from botorch.utils.multi_objective import is_non_dominated
  from botorch.utils.testing import BotorchTestCase, MockModel, MockPosterior
  from torch import Tensor
label_desc: Use image node_id 1 for calling a global function with example usage: get_model(dtype, device, num_points, input_dim, num_objectives, use_model_list, standardize_model) and returns: model,  train_X,  train_Y