class_docstr: Implements REMBO (Bayesian optimization in a linear subspace).
  
  The (D x d) projection matrix A must be provided, and must be that used for
  the initialization. In the original REMBO paper A ~ N(0, 1). Box bounds
  in the low-d space must also be provided, which in the REMBO paper should
  be [(-sqrt(d), sqrt(d)]^d.
  
  Function evaluations happen in the high-D space, and so the arms on the
  experiment will also be tracked in the high-D space. This class maintains
  a list of points in the low-d spac that have been launched, so we can match
  arms in high-D space back to their low-d point on update.
  
  Args:
      A: (D x d) projection matrix.
      initial_X_d: Points in low-d space for initial data.
      bounds_d: Box bounds in the low-d space.
      kwargs: kwargs for BotorchModel init
code: def best_point(
      self,
      search_space_digest: SearchSpaceDigest,
      torch_opt_config: TorchOptConfig,
  ) -> Optional[Tensor]:
      for b in search_space_digest.bounds:
          assert b == (-1, 1)
      assert torch_opt_config.linear_constraints is None
      assert torch_opt_config.fixed_features is None
      x_best = super().best_point(
          search_space_digest=dataclasses.replace(
              search_space_digest,
              bounds=self.bounds_d,
          ),
          torch_opt_config=torch_opt_config,
      )
      if x_best is not None:
          x_best = self.project_up(
              self.from_01(x_best.unsqueeze(0))
          ).squeeze(0)
      return x_best
  
imports: import dataclasses
  from typing import Any, List, Optional, Tuple
  from warnings import warn
  import torch
  from ax.core.search_space import SearchSpaceDigest
  from ax.core.types import TCandidateMetadata
  from ax.models.torch.botorch import BotorchModel
  from ax.models.torch_base import TorchGenResults, TorchModel, TorchOptConfig
  from ax.utils.common.docutils import copy_doc
  from botorch.utils.datasets import SupervisedDataset
  from torch import Tensor
label_desc: Use image node_id 9 for calling the REMBO obj's underlying member method code with example usage: obj.best_point(search_space_digest, torch_opt_config) and returns: x_best