code: def reset_classifier(self, num_classes, global_pool=None):
      self.num_classes = num_classes
      if global_pool is not None:
          self.pooling = SelectAdaptivePool2d(
              pool_type=global_pool, flatten=True
          )
      self.head = (
          nn.Linear(self.num_features, num_classes)
          if num_classes > 0
          else nn.Identity()
      )
  
imports: import torch
  import torch.nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import SelectAdaptivePool2d
  from ._registry import register_model, generate_default_cfgs
  from ._builder import build_model_with_cfg
  from ._manipulate import checkpoint_seq
label_desc: Use image node_id 5 for calling the ConvMixer obj's underlying member method code with example usage: obj.reset_classifier(num_classes, global_pool) without return types