code: def forward_head(self, x):
      x = self.global_pool(x)
      x = self.conv_head(x)
      x = self.act2(x)
      x = self.flatten(x)
      if self.drop_rate > 0.0:
          x = F.dropout(x, p=self.drop_rate, training=self.training)
      x = self.classifier(x)
      return x
  
imports: import copy
  from functools import partial
  import torch
  import torch.nn
  import torch.nn.functional
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
  from timm.layers import SelectAdaptivePool2d, Linear, make_divisible
  from ._builder import build_model_with_cfg
  from ._efficientnet_blocks import SqueezeExcite, ConvBnAct
  from ._manipulate import checkpoint_seq
  from ._registry import register_model, generate_default_cfgs
label_desc: Use image node_id 7 for calling the RepGhostNet obj's underlying member method code with example usage: obj.forward_head(x) and returns: x