code: import torch
  import torch.nn as nn
  import numpy as np
  
  
  __all__ = ['FixupResNet', 'fixup_resnet18', 'fixup_resnet34', 'fixup_resnet50', 'fixup_resnet101', 'fixup_resnet152']
  
  
  def conv3x3(in_planes, out_planes, stride=1):
      """3x3 convolution with padding"""
      return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                       padding=1, bias=False)
  
  
  def conv1x1(in_planes, out_planes, stride=1):
      """1x1 convolution"""
      return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
  
  
  class FixupBasicBlock(nn.Module):
      expansion = 1
  
      def __init__(self, inplanes, planes, stride=1, downsample=None):
          super(FixupBasicBlock, self).__init__()
          # Both self.conv1 and self.downsample layers downsample the input when stride != 1
          self.bias1a = nn.Parameter(torch.zeros(1))
          self.conv1 = conv3x3(inplanes, planes, stride)
          self.bias1b = nn.Parameter(torch.zeros(1))
          self.relu = nn.ReLU(inplace=True)
          self.bias2a = nn.Parameter(torch.zeros(1))
          self.conv2 = conv3x3(planes, planes)
          self.scale = nn.Parameter(torch.ones(1))
          self.bias2b = nn.Parameter(torch.zeros(1))
          self.downsample = downsample
          self.stride = stride
  
      def forward(self, x):
          identity = x
  
          out = self.conv1(x + self.bias1a)
          out = self.relu(out + self.bias1b)
  
          out = self.conv2(out + self.bias2a)
          out = out * self.scale + self.bias2b
  
          if self.downsample is not None:
              identity = self.downsample(x + self.bias1a)
  
          out += identity
          out = self.relu(out)
  
          return out
  
  
  class FixupBottleneck(nn.Module):
      expansion = 4
  
      def __init__(self, inplanes, planes, stride=1, downsample=None):
          super(FixupBottleneck, self).__init__()
          # Both self.conv2 and self.downsample layers downsample the input when stride != 1
          self.bias1a = nn.Parameter(torch.zeros(1))
          self.conv1 = conv1x1(inplanes, planes)
          self.bias1b = nn.Parameter(torch.zeros(1))
          self.bias2a = nn.Parameter(torch.zeros(1))
          self.conv2 = conv3x3(planes, planes, stride)
          self.bias2b = nn.Parameter(torch.zeros(1))
          self.bias3a = nn.Parameter(torch.zeros(1))
          self.conv3 = conv1x1(planes, planes * self.expansion)
          self.scale = nn.Parameter(torch.ones(1))
          self.bias3b = nn.Parameter(torch.zeros(1))
          self.relu = nn.ReLU(inplace=True)
          self.downsample = downsample
          self.stride = stride
  
      def forward(self, x):
          identity = x
  
          out = self.conv1(x + self.bias1a)
          out = self.relu(out + self.bias1b)
  
          out = self.conv2(out + self.bias2a)
          out = self.relu(out + self.bias2b)
  
          out = self.conv3(out + self.bias3a)
          out = out * self.scale + self.bias3b
  
          if self.downsample is not None:
              identity = self.downsample(x + self.bias1a)
  
          out += identity
          out = self.relu(out)
  
          return out
  
  
  class FixupResNet(nn.Module):
  
      def __init__(self, block, layers, num_classes=1000, zero_fc_init=True):
          super(FixupResNet, self).__init__()
          self.num_layers = sum(layers)
          self.inplanes = 64
          self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                                 bias=False)
          self.bias1 = nn.Parameter(torch.zeros(1))
          self.relu = nn.ReLU(inplace=True)
          self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
          self.layer1 = self._make_layer(block, 64, layers[0])
          self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
          self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
          self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
          self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
          self.bias2 = nn.Parameter(torch.zeros(1))
          self.fc = nn.Linear(512 * block.expansion, num_classes)
  
          for m in self.modules():
              if isinstance(m, FixupBasicBlock):
                  nn.init.normal_(m.conv1.weight, mean=0, std=np.sqrt(2 / (m.conv1.weight.shape[0] * np.prod(m.conv1.weight.shape[2:]))) * self.num_layers ** (-0.5))
                  nn.init.constant_(m.conv2.weight, 0)
                  if m.downsample is not None:
                      nn.init.normal_(m.downsample.weight, mean=0, std=np.sqrt(2 / (m.downsample.weight.shape[0] * np.prod(m.downsample.weight.shape[2:]))))
              elif isinstance(m, FixupBottleneck):
                  nn.init.normal_(m.conv1.weight, mean=0, std=np.sqrt(2 / (m.conv1.weight.shape[0] * np.prod(m.conv1.weight.shape[2:]))) * self.num_layers ** (-0.25))
                  nn.init.normal_(m.conv2.weight, mean=0, std=np.sqrt(2 / (m.conv2.weight.shape[0] * np.prod(m.conv2.weight.shape[2:]))) * self.num_layers ** (-0.25))
                  nn.init.constant_(m.conv3.weight, 0)
                  if m.downsample is not None:
                      nn.init.normal_(m.downsample.weight, mean=0, std=np.sqrt(2 / (m.downsample.weight.shape[0] * np.prod(m.downsample.weight.shape[2:]))))
              elif isinstance(m, nn.Linear) and zero_fc_init:
                  nn.init.constant_(m.weight, 0)
                  nn.init.constant_(m.bias, 0)
  
      def _make_layer(self, block, planes, blocks, stride=1):
          downsample = None
          if stride != 1 or self.inplanes != planes * block.expansion:
              downsample = conv1x1(self.inplanes, planes * block.expansion, stride)
  
          layers = []
          layers.append(block(self.inplanes, planes, stride, downsample))
          self.inplanes = planes * block.expansion
          for _ in range(1, blocks):
              layers.append(block(self.inplanes, planes))
  
          return nn.Sequential(*layers)
  
      def forward(self, x):
          x = self.conv1(x)
          x = self.relu(x + self.bias1)
          x = self.maxpool(x)
  
          x = self.layer1(x)
          x = self.layer2(x)
          x = self.layer3(x)
          x = self.layer4(x)
  
          x = self.avgpool(x)
          x = x.view(x.size(0), -1)
          x = self.fc(x + self.bias2)
  
          return x
  
  
  def fixup_resnet18(**kwargs):
      """Constructs a Fixup-ResNet-18 model.
  
      """
      model = FixupResNet(FixupBasicBlock, [2, 2, 2, 2], **kwargs)
      return model
  
  
  def fixup_resnet34(**kwargs):
      """Constructs a Fixup-ResNet-34 model.
  
      """
      model = FixupResNet(FixupBasicBlock, [3, 4, 6, 3], **kwargs)
      return model
  
  
  def fixup_resnet50(**kwargs):
      """Constructs a Fixup-ResNet-50 model.
  
      """
      model = FixupResNet(FixupBottleneck, [3, 4, 6, 3], **kwargs)
      return model
  
  
  def fixup_resnet101(**kwargs):
      """Constructs a Fixup-ResNet-101 model.
  
      """
      model = FixupResNet(FixupBottleneck, [3, 4, 23, 3], **kwargs)
      return model
  
  
  def fixup_resnet152(**kwargs):
      """Constructs a Fixup-ResNet-152 model.
  
      """
      model = FixupResNet(FixupBottleneck, [3, 8, 36, 3], **kwargs)
      return model
  
apis: torch.nn.MaxPool2d torch.ones torch.nn.Linear torch.nn.init.constant_ torch.nn.AdaptiveAvgPool2d torch.nn.Conv2d torch.nn.Sequential numpy.prod torch.zeros torch.nn.ReLU