class_docstr: Linear Elliptical Slice Sampler.
  
  Ideas:
  - Add batch support, broadcasting over parallel chains.
  - Optimize computations if possible, potentially with torch.compile.
  - Extend fixed features constraint to general linear equality constraints.
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docstr: The inverse transform of the `_transform`, i.e. maps `z` back to the original
  space where it is subject to the original constraint system (self.A, self.b).
  
  Args:
      z: The transformed tensor to be un-transformed, usually `d x 1`-dimensional.
  
  Returns:
      A `d x 1`-dimensional tensor of un-transformed values subject to the
      original system of constraints.
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code: def _untransform(self, z: Tensor) -> Tensor:
      """The inverse transform of the `_transform`, i.e. maps `z` back to the original
      space where it is subject to the original constraint system (self.A, self.b).
  
      Args:
          z: The transformed tensor to be un-transformed, usually `d x 1`-dimensional.
  
      Returns:
          A `d x 1`-dimensional tensor of un-transformed values subject to the
          original system of constraints.
      """
      if self._is_fixed is None:
          return self._unstandardize(z)
      else:
          x = self._x.clone()  # _x already contains the fixed values
          x[self._not_fixed] = self._unstandardize(z)
          return x
  
imports: from __future__ import annotations
  import math
  from typing import List, Optional, Tuple, Union
  import torch
  from botorch.utils.sampling import PolytopeSampler
  from torch import Tensor
label_desc: Use image node_id 15 for calling the LinearEllipticalSliceSampler obj's underlying member method code with example usage: obj._untransform(z) and returns: self, x