code: def forward_head(self, x, pre_logits: bool = False):
      x = self.global_pool(x)
      x = self.head_drop(x)
      if pre_logits:
          return self.flatten(x)
      x = self.fc(x)
      return self.flatten(x)
  
imports: import math
  from typing import List, Optional
  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
  from ._registry import register_model, generate_default_cfgs
label_desc: Use image node_id 8 for calling the DLA obj's underlying member method code with example usage: obj.forward_head(x, pre_logits) and returns: self, self