class_docstr: PPHGNet
  Args:
      stem_channels: list. Stem channel list of PPHGNet.
      stage_config: dict. The configuration of each stage of PPHGNet. such as the number of channels, stride, etc.
      layer_num: int. Number of layers of HG_Block.
      use_last_conv: boolean. Whether to use a 1x1 convolutional layer before the classification layer.
      class_expand: int=2048. Number of channels for the last 1x1 convolutional layer.
      dropout_prob: float. Parameters of dropout, 0.0 means dropout is not used.
      class_num: int=1000. The number of classes.
  Returns:
      model: nn.Layer. Specific PPHGNet model depends on args.
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code: def _init_weights(self):
      for m in self.sublayers():
          if isinstance(m, nn.Conv2D):
              kaiming_normal_(m.weight)
          elif isinstance(m, (nn.BatchNorm2D)):
              ones_(m.weight)
              zeros_(m.bias)
          elif isinstance(m, nn.Linear):
              zeros_(m.bias)
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddle.nn.initializer import KaimingNormal, Constant
  from paddle.nn import Conv2D, BatchNorm2D, ReLU, AdaptiveAvgPool2D, MaxPool2D
  from paddle.regularizer import L2Decay
  from paddle import ParamAttr
label_desc: Use image node_id 2 for calling the PPHGNet obj's underlying member method code with example usage: obj._init_weights() without return types