code: def _tensor_difference(A: Tensor, B: Tensor) -> Tensor:
      """Used to return B sans any Xs that also appear in A"""
      C = torch.cat((A, B), dim=0)
      D, inverse_ind = torch.unique(C, return_inverse=True, dim=0)
      n = A.shape[0]
      A_indices = inverse_ind[:n].tolist()
      B_indices = inverse_ind[n:].tolist()
      Bi_set = set(B_indices) - set(A_indices)
      return D[list(Bi_set)]
  
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 8 for calling a global function with example usage: _tensor_difference(A, B) and returns: D