class_docstr: Implement of BernNet
code: def __init__(
      self,
      input_size,
      hidden_size,
      num_class,
      K=10,
      drop=0.5,
      dprate=0.5,
      **kwargs,
  ):
      super(BernNet, self).__init__()
      self.K = K
      self.drop = drop
      self.dprate = dprate
  
      self.linear_1 = nn.Linear(input_size, hidden_size)
      self.linear_2 = nn.Linear(hidden_size, num_class)
      self.feat_dropout_1 = nn.Dropout(p=drop)
      self.feat_dropout_2 = nn.Dropout(p=dprate)
  
      self.prop = BernProp(K=self.K)
  
imports: import pgl
  import paddle.nn
  import paddle.nn.functional
  from propagation import BernProp
label_desc: Use image node_id 1 to create a new BernNet object from inherited base classes: nn with example: obj = BernNet(input_size, hidden_size, num_class, K, drop, dprate)