code: def forward_head(self, x, pre_logits: bool = False):
      x = self.global_pool(x)
      if self.drop_rate > 0.0:
          x = F.dropout(x, p=self.drop_rate, training=self.training)
      if pre_logits:
          return self.flatten(x)
      x = self.classifier(x)
      return self.flatten(x)
  
imports: from collections import OrderedDict
  from functools import partial
  from typing import Tuple
  import torch
  import torch.nn
  import torch.nn.functional
  from timm.data import IMAGENET_DPN_MEAN, IMAGENET_DPN_STD, IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import BatchNormAct2d, ConvNormAct, create_conv2d, create_classifier, get_norm_act_layer
  from ._builder import build_model_with_cfg
  from ._registry import register_model, generate_default_cfgs
label_desc: Use image node_id 7 for calling the DPN obj's underlying member method code with example usage: obj.forward_head(x, pre_logits) and returns: self, self