code: def _normalize_input(dummy_input: Any) -> Any:
      if isinstance(dummy_input, torch.Tensor):
          dummy_input = (dummy_input,)
      elif isinstance(dummy_input, list):
          dummy_input = tuple(dummy_input)
      elif isinstance(dummy_input, dict):
          dummy_input = tuple(dummy_input.values())
      return dummy_input
  
imports: from __future__ import annotations
  import inspect
  import logging
  from pathlib import Path
  import tempfile
  from typing import Any, Dict, List
  import torch
  import torch.fx
  from torch.fx import GraphModule
  from torch.fx._compatibility import compatibility
  from torch.fx.node import Node, Target
  from torch.fx.passes.shape_prop import ShapeProp
  from nni.common.concrete_trace_utils import concrete_trace
  from nni.compression.utils import set_nested_attr
  from nni.compression.speedup.replacement import replace_module
  from .container import NodeInfo
  from .mask_conflict import fix_channel_mask_conflict, fix_group_mask_conflict, fix_weight_sharing_mask_conflict
  from .mask_updater import MaskUpdater, DefaultMaskUpdater, LeafModuleMaskUpdater, NoMaskUpdater, NoChangeMaskUpdater
  from .replacement import replace_module
  from .replacer import Replacer, DefaultReplacer
  from .utils import tree_map_zip, poss_deepcopy, randomize_like_with_shape, torch_integer_dtype
  from ..utils import set_nested_attr
label_desc: Use image node_id 1 for calling a global function with example usage: _normalize_input(dummy_input) and returns: dummy_input