code: def get_index_tensors(
      fixed_indices: Union[List[int], Tensor], d: int
  ) -> Tuple[Tensor, Tensor]:
      """Converts `fixed_indices` to a `d`-dim integral Tensor that is True at indices
      that are contained in `fixed_indices` and False otherwise.
  
      Args:
          fixed_indices: A list or Tensoro of integer indices to fix.
          d: The dimensionality of the Tensors to be indexed.
  
      Returns:
          A Tuple of integral Tensors partitioning [1, d] into indices that are fixed
          (first tensor) and non-fixed (second tensor).
      """
      is_fixed = torch.as_tensor(fixed_indices)
      dtype, device = is_fixed.dtype, is_fixed.device
      dims = torch.arange(d, dtype=dtype, device=device)
      not_fixed = torch.tensor([i for i in dims if i not in is_fixed])
      return is_fixed, not_fixed
  
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 1 for calling a global function with example usage: get_index_tensors(fixed_indices, d) and returns: is_fixed,  not_fixed