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 cross_validate(
      self,
      datasets: List[SupervisedDataset],
      X_test: Tensor,
      **kwargs: Any,
  ) -> Tuple[Tensor, Tensor]:
      low_d_datasets = self._convert_and_normalize_datasets(
          datasets=datasets
      )
      X_test_d = self.project_down(X_test)
      return super().cross_validate(
          datasets=low_d_datasets,
          X_test=self.to_01(X_test_d),
      )
  
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 10 for calling the REMBO obj's underlying member method code with example usage: obj.cross_validate(datasets, X_test) and returns: super