code: def forward(self, x):
      x = self.forward_features(x)
      x = x.flatten(1)
      if self.drop_rate > 0.0:
          x = F.dropout(x, p=self.drop_rate, training=self.training)
      return self.classifier(x)
  
imports: from lib.utils.builder_util import *
  from lib.utils.search_structure_supernet import *
  from lib.models.builders.build_supernet import *
  from lib.utils.op_by_layer_dict import flops_op_dict
  from timm.models.layers import SelectAdaptivePool2d
  from timm.models.layers.activations import hard_sigmoid
label_desc: Use image node_id 5 for calling the SuperNet obj's underlying member method code with example usage: obj.forward(x) and returns: self