code: def PPHGNet_base(pretrained=False, use_ssld=True, **kwargs):
      """
      PPHGNet_base
      Args:
          pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
                      If str, means the path of the pretrained model.
          use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
      Returns:
          model: nn.Layer. Specific `PPHGNet_base` model depends on args.
      """
      stage_config = {
          # in_channels, mid_channels, out_channels, blocks, downsample
          "stage1": [160, 192, 320, 1, False, [2, 1]],
          "stage2": [320, 224, 640, 2, True, [1, 2]],
          "stage3": [640, 256, 960, 3, True, [2, 1]],
          "stage4": [960, 288, 1280, 2, True, [2, 1]],
      }
  
      model = PPHGNet(
          stem_channels=[96, 96, 160],
          stage_config=stage_config,
          layer_num=7,
          dropout_prob=0.2,
          **kwargs,
      )
      return model
  
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 3 for calling a global function with example usage: PPHGNet_base(pretrained, use_ssld) and returns: model