code: def normalize_linear_constraints(
      bounds: Tensor, constraints: List[Tuple[Tensor, Tensor, float]]
  ) -> List[Tuple[Tensor, Tensor, float]]:
      r"""Normalize linear constraints to the unit cube.
  
      Args:
          bounds (Tensor): A `2 x d`-dim tensor containing the box bounds.
          constraints (List[Tuple[Tensor, Tensor, float]]): A list of
              tuples (indices, coefficients, rhs), with each tuple encoding
              an inequality constraint of the form
              `\sum_i (X[indices[i]] * coefficients[i]) >= rhs` or
              `\sum_i (X[indices[i]] * coefficients[i]) = rhs`.
      """
  
      new_constraints = []
      for index, coefficient, rhs in constraints:
          lower, upper = bounds[:, index]
          s = upper - lower
          new_constraints.append(
              (
                  index,
                  s * coefficient,
                  (rhs - torch.dot(coefficient, lower)).item(),
              )
          )
      return new_constraints
  
imports: from __future__ import annotations
  from abc import ABC, abstractmethod
  from contextlib import contextmanager
  from typing import Any, Generator, Iterable, List, Optional, Tuple, TYPE_CHECKING
  import numpy
  import scipy
  import torch
  from botorch.exceptions.errors import BotorchError
  from botorch.sampling.qmc import NormalQMCEngine
  from botorch.utils.transforms import unnormalize
  from scipy.spatial import Delaunay, HalfspaceIntersection
  from torch import LongTensor, Tensor
  from torch.quasirandom import SobolEngine
label_desc: Use image node_id 10 for calling a global function with example usage: normalize_linear_constraints(bounds, constraints) and returns: new_constraints