code: def list_mode_options(
      mode: Optional[str] = None, dynamic: Optional[bool] = None
  ) -> Dict[str, Any]:
      r"""Returns a dictionary describing the optimizations that each of the available
      modes passed to `torch.compile()` performs.
  
      Args:
          mode (str, optional): The mode to return the optimizations for.
          If None, returns optimizations for all modes
          dynamic (bool, optional): Whether dynamic shape is enabled.
  
      Example::
          >>> torch._inductor.list_mode_options()
      """
  
      mode_options: Dict[str, Dict[str, bool]] = {
          "default": {},
          # enable cudagraphs
          "reduce-overhead": {
              "triton.cudagraphs": True,
          },
          # enable max-autotune
          "max-autotune-no-cudagraphs": {
              "max_autotune": True,
          },
          # enable max-autotune
          # enable cudagraphs
          "max-autotune": {
              "max_autotune": True,
              "triton.cudagraphs": True,
          },
      }
      return mode_options[mode] if mode else mode_options
  
file_path: pytorch/torch/_inductor/__init__.py
imports: from typing import Any, Dict, List, Optional
  import torch.fx
  import torch.utils._pytree
label_desc: Use image node_id 3 for calling a global function with example usage: list_mode_options(mode, dynamic) and returns: unknown