code: def group_matcher(self, coarse=False):
      module_map = {
          k: i
          for i, (k, _) in enumerate(
              flatten_modules(self.named_children(), prefix=())
          )
      }
      module_map.pop(("classif",))
  
      def _matcher(name):
          if any(
              [name.startswith(n) for n in ("conv2d_1", "conv2d_2")]
          ):
              return 0
          elif any(
              [name.startswith(n) for n in ("conv2d_3", "conv2d_4")]
          ):
              return 1
          elif any(
              [name.startswith(n) for n in ("block8", "conv2d_7")]
          ):
              return len(module_map) + 1
          else:
              for k in module_map.keys():
                  if k == tuple(name.split(".")[: len(k)]):
                      return module_map[k]
              return float("inf")
  
      return _matcher
  
imports: from functools import partial
  import torch
  import torch.nn
  import torch.nn.functional
  from timm.data import IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD
  from timm.layers import create_classifier, ConvNormAct
  from ._builder import build_model_with_cfg
  from ._manipulate import flatten_modules
  from ._registry import register_model, generate_default_cfgs, register_model_deprecations
label_desc: Use image node_id 2 for calling the InceptionResnetV2 obj's underlying member method code with example usage: obj.group_matcher(coarse) and returns: _matcher, int, int, unknown, float, module_map