text: import torch
  import torch.nn as nn
  import torch.nn.functional as F
  from operations import NaiveBN
  
  
  class Decoder(nn.Module):
      def __init__(self, num_classes, filter_multiplier, BatchNorm=NaiveBN, args=None, last_level=0):
          super(Decoder, self).__init__()
          low_level_inplanes = filter_multiplier
          C_low = 48
          self.conv1 = nn.Conv2d(low_level_inplanes, C_low, 1, bias=False)
          self.bn1 = BatchNorm(48)
          self.last_conv = nn.Sequential(nn.Conv2d(304,256, kernel_size=3, stride=1, padding=1, bias=False),
                                         BatchNorm(256),
                                         nn.Dropout(0.5),
                                         nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1, bias=False),
                                         BatchNorm(256),
                                         nn.Dropout(0.1),
                                         nn.Conv2d(256, num_classes, kernel_size=1, stride=1))
          self._init_weight()
  
      def forward(self, x, low_level_feat):
          low_level_feat = self.conv1(low_level_feat)
          low_level_feat = self.bn1(low_level_feat)
  
          x = F.interpolate(x, size=low_level_feat.size()[2:], mode='bilinear', align_corners=True)
          x = torch.cat((x, low_level_feat), dim=1)
          x = self.last_conv(x)
          return x
  
      def _init_weight(self):
          for m in self.modules():
              if isinstance(m, nn.Conv2d):
                  torch.nn.init.kaiming_normal_(m.weight)
              elif isinstance(m, nn.BatchNorm2d):
                  m.weight.data.fill_(1)
                  m.bias.data.zero_()