code: def reset_classifier(self, num_classes, global_pool="avg"):
      self.num_classes = num_classes
      self.global_pool, self.classifier = create_classifier(
          self.num_features, self.num_classes, pool_type=global_pool
      )
  
imports: import logging
  from typing import List
  import torch
  import torch.nn
  import torch.nn.functional
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import create_classifier
  from ._builder import build_model_with_cfg, pretrained_cfg_for_features
  from ._features import FeatureInfo
  from ._registry import register_model, generate_default_cfgs
  from .resnet import BasicBlock, Bottleneck
label_desc: Use image node_id 10 for calling the HighResolutionNet obj's underlying member method code with example usage: obj.reset_classifier(num_classes, global_pool) without return types