text: import torch
  from torch import nn
  import torch.nn.functional as F
  from torchvision.models._utils import IntermediateLayerGetter
  
  class ASPP_Classifier_V2(nn.Module):
      def __init__(self, in_channels, dilation_series, padding_series, num_classes):
          super(ASPP_Classifier_V2, self).__init__()
          self.conv2d_list = nn.ModuleList()
          for dilation, padding in zip(dilation_series, padding_series):
              self.conv2d_list.append(
                  nn.Conv2d(
                      in_channels,
                      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)
  
      def forward(self, x, size=None):
          out = self.conv2d_list[0](x)
          for i in range(len(self.conv2d_list) - 1):
              out += self.conv2d_list[i + 1](x)
          if size is not None:
              out = F.interpolate(out, size=size, mode='bilinear', align_corners=True)
          return out