code: def sparse_to_dense_constraints(
      d: int,
      constraints: List[Tuple[Tensor, Tensor, float]],
  ) -> Tuple[Tensor, Tensor]:
      r"""Convert parameter constraints from a sparse format into a dense format.
  
      This method converts sparse triples of the form (indices, coefficients, rhs)
      to constraints of the form Ax >= b or Ax = b.
  
      Args:
          d: The input dimension.
          inequality 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` or
              `\sum_i (X[indices[i]] * coefficients[i]) = rhs`.
  
      Returns:
          A two-element tuple containing:
              - A: A `n_constraints x d`-dim tensor of coefficients.
              - b: A `n_constraints x 1`-dim tensor of right hand sides.
      """
      _t = constraints[0][1]
      A = torch.zeros(
          len(constraints), d, dtype=_t.dtype, device=_t.device
      )
      b = torch.zeros(
          len(constraints), 1, dtype=_t.dtype, device=_t.device
      )
      for i, (indices, coefficients, rhs) in enumerate(constraints):
          A[i, indices.long()] = coefficients
          b[i] = rhs
      return A, b
  
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 12 for calling a global function with example usage: sparse_to_dense_constraints(d, constraints) and returns: A,  b