code: def get_unmatchable_types_map() -> Dict[str, Set[NSNodeTargetType]]:
      FUNS_UNMATCHABLE: Set[NSNodeTargetType] = {
          torch.quantize_per_tensor,
          operator.getitem,
      }
  
      MODS_UNMATCHABLE: Set[NSNodeTargetType] = {
          nn.Identity,
      }
  
      METHS_UNMATCHABLE: Set[NSNodeTargetType] = {
          "to",
          "dequantize",
          "reshape",
          "view",
          "unsqueeze_",
          "unsqueeze",
          "transpose",
          "squeeze_",
          "squeeze",
          "size",
          "shape",
          "resize_",
          "repeat_interleave",
          "repeat",
          "permute",
          "numel",
          "mean",
          "detach_",
          "detach",
          "contiguous",
          "clamp",
          "chunk",
      }
  
      return {
          "funs_unmatchable": FUNS_UNMATCHABLE,
          "mods_unmatchable": MODS_UNMATCHABLE,
          "meths_unmatchable": METHS_UNMATCHABLE,
      }
  
imports: import operator
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.ao.nn.quantized
  import torch.ao.nn.quantized.dynamic
  import torch.ao.nn.intrinsic.quantized
  import torch.ao.nn.intrinsic.quantized.dynamic
  import torch.ao.nn.intrinsic.qat
  import torch.ao.nn.intrinsic
  import torch.ao.nn.qat
  import torch.ao.nn.qat.dynamic
  from torch.ao.quantization.backend_config import get_native_backend_config
  import torch.ao.quantization.fx._lower_to_native_backend
  import torch.ao.quantization.quantization_mappings
  from .ns_types import NSNodeTargetType
  from typing import Callable, Dict, List, Optional, Set, Tuple
label_desc: Use image node_id 5 for calling a global function with example usage: get_unmatchable_types_map() and returns: dict