code: def forward(self, x):
      features = super(_DenseASPPConv, self).forward(x)
      if self.drop_rate > 0:
          features = F.dropout(
              features, p=self.drop_rate, training=self.training
          )
      return features
  
imports: import torch
  import torch.nn
  import torch.nn.functional
  from pywick.models.segmentation.da_basenets.densenet import *
  from pywick.models.segmentation.da_basenets.fcn import _FCNHead
label_desc: Use image node_id 2 for calling the _DenseASPPConv obj's underlying member method code with example usage: obj.forward(x) and returns: features