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: Transforms the input so that it is equivalent to a standard Normal variable
  constrained with the modified system constraints (self._Az, self._bz).
  
  Args:
      x: The input tensor to be transformed, usually `d x 1`-dimensional.
  
  Returns:
      A `d x 1`-dimensional tensor of transformed values subject to the modified
      system of constraints.
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code: def _transform(self, x: Tensor) -> Tensor:
      """Transforms the input so that it is equivalent to a standard Normal variable
      constrained with the modified system constraints (self._Az, self._bz).
  
      Args:
          x: The input tensor to be transformed, usually `d x 1`-dimensional.
  
      Returns:
          A `d x 1`-dimensional tensor of transformed values subject to the modified
          system of constraints.
      """
      if self._not_fixed is not None:
          x = x[self._not_fixed]
      return self._standardize(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 14 for calling the LinearEllipticalSliceSampler obj's underlying member method code with example usage: obj._transform(x) and returns: self