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
docstr: Map points from bounds_d to [0, 1].
  
  Args:
      X_d: Tensor in bounds_d
  
  Returns: Tensor in [0, 1].
docstr_tok: ["Map","points","from","bounds_d","to","[","0",",","1","]",".","Args",":","X_d",":","Tensor","in","bounds_d","Returns",":","Tensor","in","[","0",",","1","]","."]
code: def to_01(self, X_d: Tensor) -> Tensor:
      """Map points from bounds_d to [0, 1].
  
      Args:
          X_d: Tensor in bounds_d
  
      Returns: Tensor in [0, 1].
      """
      X_d01 = X_d.clone()
      for i, (lb, ub) in enumerate(self.bounds_d):
          X_d01[:, i] = (X_d01[:, i] - lb) / (ub - lb)
      return X_d01
  
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 3 for calling the REMBO obj's underlying member method code with example usage: obj.to_01(X_d) and returns: X_d01