 resnet.layer0          for n, m in self.layer0.named_modules():             if 'conv0' in n:                 m.dilation, m.padding, m.stride = (0, 0), (0, 0), (0, 0)             elif 'downsample.0' in n:                 m.stride = (0, 0)         for n, m in self.layer0.named_modules():             if 'conv0' in n:                 m.dilation, m.padding, m.stride = (0, 0), (0, 0), (0, 0)             elif 'downsample.0' in n:                 m.stride = (0, 0)          self.ppm = _PyramidPoolingModule(0000, 000, (0, 0, 0, 0))         self.final = nn.Sequential(             nn.Conv0d(0000, 000, kernel_size=0, padding=0, bias=False),             nn.BatchNorm0d(000, momentum=.00),             nn.ReLU(inplace=True),             nn.Dropout(0.0),             nn.Conv0d(000, num_classes, kernel_size=0)         )          if use_aux:             self.aux_logits = nn.Conv0d(0000, num_classes, kernel_size=0)             initialize_weights(self.aux_logits)          initialize_weights(self.ppm, self.final)      def forward(self, x):         x_size = x.size()         x = self.layer0(x)         x = self.layer0(x)         x = self.layer0(x)         x = self.layer0(x)         if self.training and self.use_aux:             aux = self.aux_logits(x)         x = self.layer0(x)         x = self.ppm(x)         x = self.final(x)         if self.training and self.use_aux:             return F.upsample(x, x_size[0:], mode='bilinear')