code: def isolate_rng(
      include_cuda: bool = True,
  ) -> Generator[None, None, None]:
      """A context manager that resets the global random state on exit to what it was before entering.
  
      It supports isolating the states for PyTorch, Numpy, and Python built-in random number generators.
  
      Args:
          include_cuda: Whether to allow this function to also control the `torch.cuda` random number generator.
              Set this to ``False`` when using the function in a forked process where CUDA re-initialization is
              prohibited.
  
      Example:
          >>> import torch
          >>> torch.manual_seed(1)  # doctest: +ELLIPSIS
          <torch._C.Generator object at ...>
          >>> with isolate_rng():
          ...     [torch.rand(1) for _ in range(3)]
          [tensor([0.7576]), tensor([0.2793]), tensor([0.4031])]
          >>> torch.rand(1)
          tensor([0.7576])
  
      """
      states = _collect_rng_states(include_cuda)
      yield
      _set_rng_states(states)
  
label_desc: Use image node_id 1 for calling a global function with example usage: isolate_rng(include_cuda) without return types