code: def L1_norm_func(X: Tensor, init_point: Tensor) -> Tensor:
      r"""L1_norm takes in a a `batch_shape x n x d`-dim input tensor `X`
      to a `batch_shape x n x 1`-dimensional L1 norm tensor. To be used
      for constructing a GenericDeterministicModel.
      """
      return torch.linalg.norm(
          (X - init_point), ord=1, dim=-1, keepdim=True
      )
  
imports: import functools
  from copy import deepcopy
  from functools import partial
  from typing import Any, Callable, Dict, List, Optional, Tuple, Type
  import torch
  from ax.core.search_space import SearchSpaceDigest
  from ax.models.torch.botorch_modular.acquisition import Acquisition
  from ax.models.torch.botorch_modular.optimizer_argparse import optimizer_argparse
  from ax.models.torch.botorch_modular.surrogate import Surrogate
  from ax.models.torch_base import TorchOptConfig
  from ax.utils.common.constants import Keys
  from ax.utils.common.typeutils import not_none
  from botorch.acquisition.acquisition import AcquisitionFunction
  from botorch.acquisition.multi_objective.monte_carlo import qExpectedHypervolumeImprovement
  from botorch.acquisition.penalized import L0Approximation
  from botorch.models.deterministic import GenericDeterministicModel
  from botorch.models.model import ModelList
  from botorch.optim import Homotopy, HomotopyParameter, LogLinearHomotopySchedule, optimize_acqf_homotopy
  from botorch.utils.datasets import SupervisedDataset
  from botorch.utils.multi_objective.pareto import is_non_dominated
  from torch import Tensor
  from torch.quasirandom import SobolEngine
label_desc: Use image node_id 1 for calling a global function with example usage: L1_norm_func(X, init_point) and returns: torch