code: def get_rembo_initializer(
      search_space: SearchSpace,
      A: np.ndarray,
      bounds_d: List[Tuple[float, float]],
      seed: Optional[int] = None,
      **kwargs: Any,
  ) -> RandomModelBridge:
      """Instantiates a uniform random generator.
  
      Args:
          search_space: Search space.
          A: Projection matrix.
          bounds_d: Bounds in low-d space.
          seed: seed.
          kwargs: kwargs
  
      Returns:
          RandomModelBridge, with REMBOInitializer as model.
      """
      return RandomModelBridge(
          search_space=search_space,
          model=REMBOInitializer(
              A=A, bounds_d=bounds_d, seed=seed, **kwargs
          ),
          transforms=[CenteredUnitX],
      )
  
imports: import math
  from collections import defaultdict
  from typing import Any, Dict, List, Optional, Set, Tuple
  import numpy
  import torch
  from ax.core.data import Data
  from ax.core.experiment import Experiment
  from ax.core.generator_run import GeneratorRun
  from ax.core.search_space import SearchSpace
  from ax.modelbridge.factory import DEFAULT_TORCH_DEVICE
  from ax.modelbridge.generation_strategy import GenerationStep, GenerationStrategy
  from ax.modelbridge.random import RandomModelBridge
  from ax.modelbridge.torch import TorchModelBridge
  from ax.modelbridge.transforms.centered_unit_x import CenteredUnitX
  from ax.modelbridge.transforms.standardize_y import StandardizeY
  from ax.models.random.rembo_initializer import REMBOInitializer
  from ax.models.torch.rembo import REMBO
  from ax.utils.common.typeutils import not_none
label_desc: Use image node_id 1 for calling a global function with example usage: get_rembo_initializer(search_space, A, bounds_d, seed) and returns: RandomModelBridge