code: def __init__(
      self, dilation_series, padding_series, num_classes, inplane
  ):
      super(Classifier_Module, self).__init__()
      self.conv2d_list = nn.ModuleList()
      for dilation, padding in zip(dilation_series, padding_series):
          self.conv2d_list.append(
              nn.Conv2d(
                  inplane,
                  num_classes,
                  kernel_size=3,
                  stride=1,
                  padding=padding,
                  dilation=dilation,
                  bias=True,
              )
          )
  
      for m in self.conv2d_list:
          m.weight.data.normal_(0, 0.01)
  
imports: import math
  import torch
  import torch.nn
  from torch.nn.parameter import Parameter
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new Classifier_Module object from inherited base classes: nn with example: obj = Classifier_Module(dilation_series, padding_series, num_classes, inplane)