code: def dummy_predict(model, X) -> Tuple[Tensor, Tensor]:
      # Add column to X that is a product of previous elements.
      mean = torch.cat([X, torch.prod(X, dim=1).reshape(-1, 1)], dim=1)
      cov = torch.zeros(mean.shape[0], mean.shape[1], mean.shape[1])
      return mean, cov
  
imports: from contextlib import ExitStack
  from typing import Any, cast, Dict, Tuple
  from unittest import mock
  import numpy
  import torch
  from ax.core.search_space import SearchSpaceDigest
  from ax.models.torch.botorch_moo import MultiObjectiveBotorchModel
  from ax.models.torch.botorch_moo_defaults import get_outcome_constraint_transforms, get_qLogEHVI, get_qLogNEHVI, get_weighted_mc_objective_and_objective_thresholds, infer_objective_thresholds, pareto_frontier_evaluator
  from ax.models.torch.utils import _get_X_pending_and_observed
  from ax.utils.common.testutils import TestCase
  from botorch.utils.datasets import SupervisedDataset
  from botorch.utils.multi_objective.hypervolume import infer_reference_point
  from botorch.utils.sampling import manual_seed
  from botorch.utils.testing import MockModel, MockPosterior
  from torch._tensor import Tensor
label_desc: Use image node_id 1 for calling a global function with example usage: dummy_predict(model, X) and returns: mean,  cov