code: def __init__(
      self, extract_embedding: bool = True, checkpoint: str = None
  ):
      super(CNN10, self).__init__()
      self.bn0 = nn.BatchNorm2D(64)
      self.conv_block1 = ConvBlock(in_channels=1, out_channels=64)
      self.conv_block2 = ConvBlock(in_channels=64, out_channels=128)
      self.conv_block3 = ConvBlock(in_channels=128, out_channels=256)
      self.conv_block4 = ConvBlock(in_channels=256, out_channels=512)
  
      self.fc1 = nn.Linear(512, self.emb_size)
      self.fc_audioset = nn.Linear(self.emb_size, 527)
  
      if checkpoint is not None and os.path.isfile(checkpoint):
          state_dict = paddle.load(checkpoint)
          self.set_state_dict(state_dict)
          print(
              f"Loaded CNN10 pretrained parameters from: {checkpoint}"
          )
      else:
          print(
              "No valid checkpoints for CNN10. Start training from scratch."
          )
  
      self.extract_embedding = extract_embedding
  
file_path: PaddleHub/modules/audio/audio_classification/PANNs/cnn10/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 1 to create a new CNN10 object from inherited base classes: nn with example: obj = CNN10(extract_embedding, checkpoint)