code: def transform_constraints(
      constraints: Union[List[Tuple[Tensor, Tensor, float]], None],
      q: int,
      d: int,
  ) -> List[Tuple[Tensor, Tensor, float]]:
      r"""Transform constraints to sample from a d*q-dimensional space instead of a
      d-dimensional state.
  
      This function assumes that constraints are the same for each input batch,
      and broadcasts the constraints accordingly to the input batch shape.
  
      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`.
              If `indices` is a 2-d Tensor, this supports specifying constraints across
              the points in the `q`-batch (inter-point constraints). If `None`, this
              function is a nullop and simply returns `None`.
          q: Size of the `q`-batch.
          d: Dimensionality of the problem.
  
      Returns:
          List[Tuple[Tensor, Tensor, float]]: List of transformed constraints.
      """
      if constraints is None:
          return None
      transformed = []
      for constraint in constraints:
          if len(constraint[0].shape) == 1:
              transformed += transform_intra_point_constraint(
                  constraint, d, q
              )
          else:
              transformed.append(
                  transform_inter_point_constraint(constraint, d)
              )
      return transformed
  
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 1 for calling a global function with example usage: transform_constraints(constraints, q, d) and returns: transformed, None