code: def forward(self, x):
      x.stop_gradient = False
      x = x.transpose([0, 3, 2, 1])
      x = self.bn0(x)
      x = x.transpose([0, 3, 2, 1])
  
      x = self.conv_block1(x, pool_size=(2, 2), pool_type="avg")
      x = F.dropout(x, p=0.2, training=self.training)
  
      x = self.conv_block2(x, pool_size=(2, 2), pool_type="avg")
      x = F.dropout(x, p=0.2, training=self.training)
  
      x = self.conv_block3(x, pool_size=(2, 2), pool_type="avg")
      x = F.dropout(x, p=0.2, training=self.training)
  
      x = self.conv_block4(x, pool_size=(2, 2), pool_type="avg")
      x = F.dropout(x, p=0.2, training=self.training)
  
      x = x.mean(axis=3)
      x = x.max(axis=2) + x.mean(axis=2)
  
      x = F.dropout(x, p=0.5, training=self.training)
      x = F.relu(self.fc1(x))
  
      if self.extract_embedding:
          output = F.dropout(x, p=0.5, training=self.training)
      else:
          output = F.sigmoid(self.fc_audioset(x))
      return output
  
file_path: PaddleHub/modules/audio/audio_classification/PANNs/cnn6/network.py
imports: import os
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddlehub.utils.log import logger
label_desc: Use image node_id 2 for calling the CNN6 obj's underlying member method code with example usage: obj.forward(x) and returns: output