code: def transform_inter_point_constraint(
      constraint: Tuple[Tensor, Tensor, float], d: int
  ) -> Tuple[Tensor, Tensor, float]:
      r"""Transforms an inter-point constraint from
      d-dimensional space to a d*q dimesional space.
  
      Args:
          constraints: A list of tuples (indices, coefficients, rhs), with each tuple
              encoding an (in-)equality constraint of the form
              `\sum_i (X[indices[i]] * coefficients[i]) (>)= rhs`. `indices` must be a
              2-d Tensor, where in each row `indices[i] = (k_i, l_i)` the first index
              `k_i` corresponds to the `k_i`-th element of the `q`-batch and the second
              index `l_i` corresponds to the `l_i`-th feature of that element.
  
      Raises:
          ValueError: If indices in the constraints are larger than the
              dimensionality d of the problem.
  
      Returns:
          List[Tuple[Tensor, Tensor, float]]: Transformed constraint.
      """
      indices, coefficients, rhs = constraint
      if indices[:, 1].max() >= d:
          raise ValueError(
              f"Constraint indices cannot exceed the problem dimension {d=}."
          )
      return (
          torch.tensor(
              [r[0] * d + r[1] for r in indices],
              dtype=torch.int64,
              device=indices.device,
          ),
          coefficients,
          rhs,
      )
  
imports: from __future__ import annotations
  import warnings
  from math import ceil
  from typing import Callable, Dict, List, Optional, Tuple, Union
  import torch
  from botorch import settings
  from botorch.acquisition import analytic, monte_carlo, multi_objective
  from botorch.acquisition.acquisition import AcquisitionFunction
  from botorch.acquisition.fixed_feature import FixedFeatureAcquisitionFunction
  from botorch.acquisition.knowledge_gradient import _get_value_function, qKnowledgeGradient
  from botorch.acquisition.multi_objective.hypervolume_knowledge_gradient import _get_hv_value_function, qHypervolumeKnowledgeGradient, qMultiFidelityHypervolumeKnowledgeGradient
  from botorch.exceptions.errors import BotorchTensorDimensionError, UnsupportedError
  from botorch.exceptions.warnings import BadInitialCandidatesWarning, BotorchWarning, SamplingWarning
  from botorch.models.model import Model
  from botorch.optim.utils import fix_features, get_X_baseline
  from botorch.utils.multi_objective.pareto import is_non_dominated
  from botorch.utils.sampling import batched_multinomial, draw_sobol_samples, get_polytope_samples, manual_seed
  from botorch.utils.transforms import normalize, standardize, unnormalize
  from torch import Tensor
  from torch.distributions import Normal
  from torch.quasirandom import SobolEngine
label_desc: Use image node_id 3 for calling a global function with example usage: transform_inter_point_constraint(constraint, d) and returns: torch,  coefficients,  rhs