code: def __init__(
      self, inplanes, dilation_series, padding_series, num_classes
  ):
      super(ClassifierModule, self).__init__()
      self.conv2d_list = nn.LayerList()
      for dilation, padding in zip(dilation_series, padding_series):
          weight_attr = paddle.ParamAttr(
              initializer=nn.initializer.Normal(std=0.01),
              learning_rate=10.0,
          )
          bias_attr = paddle.ParamAttr(
              initializer=nn.initializer.Constant(value=0.0),
              learning_rate=10.0,
          )
          self.conv2d_list.append(
              nn.Conv2D(
                  inplanes,
                  num_classes,
                  kernel_size=3,
                  stride=1,
                  padding=padding,
                  dilation=dilation,
                  weight_attr=weight_attr,
                  bias_attr=bias_attr,
              )
          )
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  from paddleseg.cvlibs import manager
  from paddleseg.models import layers
  from paddleseg.utils import utils
label_desc: Use image node_id 1 to create a new ClassifierModule object from inherited base classes: nn with example: obj = ClassifierModule(inplanes, dilation_series, padding_series, num_classes)