code: import torch
  import torch.nn as nn
  import torch.nn.functional as F
  from torch.autograd import Variable
  import math
  from functools import partial
  
  __all__ = [
      'ResNet', 'resnet10', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
      'resnet152', 'resnet200'
  ]
  
  
  def conv3x3x3(in_planes, out_planes, stride=1):
      # 3x3x3 convolution with padding
      return nn.Conv3d(
          in_planes,
          out_planes,
          kernel_size=3,
          stride=stride,
          padding=1,
          bias=False)
  
  
  def downsample_basic_block(x, planes, stride):
      out = F.avg_pool3d(x, kernel_size=1, stride=stride)
      zero_pads = torch.Tensor(
          out.size(0), planes - out.size(1), out.size(2), out.size(3),
          out.size(4)).zero_()
      if isinstance(out.data, torch.cuda.FloatTensor):
          zero_pads = zero_pads.cuda()
  
      out = Variable(torch.cat([out.data, zero_pads], dim=1))
  
      return out
  
  
  class BasicBlock(nn.Module):
      expansion = 1
  
      def __init__(self, inplanes, planes, stride=1, downsample=None):
          super(BasicBlock, self).__init__()
          self.conv1 = conv3x3x3(inplanes, planes, stride)
          self.bn1 = nn.BatchNorm3d(planes)
          self.relu = nn.ReLU(inplace=True)
          self.conv2 = conv3x3x3(planes, planes)
          self.bn2 = nn.BatchNorm3d(planes)
          self.downsample = downsample
          self.stride = stride
  
      def forward(self, x):
          residual = x
  
          out = self.conv1(x)
          out = self.bn1(out)
          out = self.relu(out)
  
          out = self.conv2(out)
          out = self.bn2(out)
  
          if self.downsample is not None:
              residual = self.downsample(x)
  
          out += residual
          out = self.relu(out)
  
          return out
  
  
  class Bottleneck(nn.Module):
      expansion = 4
  
      def __init__(self, inplanes, planes, stride=1, downsample=None):
          super(Bottleneck, self).__init__()
          self.conv1 = nn.Conv3d(inplanes, planes, kernel_size=1, bias=False)
          self.bn1 = nn.BatchNorm3d(planes)
          self.conv2 = nn.Conv3d(
              planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
          self.bn2 = nn.BatchNorm3d(planes)
          self.conv3 = nn.Conv3d(planes, planes * 4, kernel_size=1, bias=False)
          self.bn3 = nn.BatchNorm3d(planes * 4)
          self.relu = nn.ReLU(inplace=True)
          self.downsample = downsample
          self.stride = stride
  
      def forward(self, x):
          residual = x
  
          out = self.conv1(x)
          out = self.bn1(out)
          out = self.relu(out)
  
          out = self.conv2(out)
          out = self.bn2(out)
          out = self.relu(out)
  
          out = self.conv3(out)
          out = self.bn3(out)
  
          if self.downsample is not None:
              residual = self.downsample(x)
  
          out += residual
          out = self.relu(out)
  
          return out
  
  
  class ResNet(nn.Module):
  
      def __init__(self,
                   block,
                   layers,
                   sample_size,
                   sample_duration,
                   shortcut_type='B',
                   num_classes=400):
          self.inplanes = 64
          super(ResNet, self).__init__()
          self.conv1 = nn.Conv3d(
              3,
              64,
              kernel_size=7,
              stride=(1, 2, 2),
              padding=(3, 3, 3),
              bias=False)
          self.bn1 = nn.BatchNorm3d(64)
          self.relu = nn.ReLU(inplace=True)
          self.maxpool = nn.MaxPool3d(kernel_size=(3, 3, 3), stride=2, padding=1)
          self.layer1 = self._make_layer(block, 64, layers[0], shortcut_type)
          self.layer2 = self._make_layer(
              block, 128, layers[1], shortcut_type, stride=2)
          self.layer3 = self._make_layer(
              block, 256, layers[2], shortcut_type, stride=2)
          self.layer4 = self._make_layer(
              block, 512, layers[3], shortcut_type, stride=2)
          last_duration = int(math.ceil(sample_duration / 16))
          last_size = int(math.ceil(sample_size / 32))
          self.avgpool = nn.AvgPool3d(
              (last_duration, last_size, last_size), stride=1)
          self.fc = nn.Linear(512 * block.expansion, num_classes)
  
          for m in self.modules():
              if isinstance(m, nn.Conv3d):
                  m.weight = nn.init.kaiming_normal(m.weight, mode='fan_out')
              elif isinstance(m, nn.BatchNorm3d):
                  m.weight.data.fill_(1)
                  m.bias.data.zero_()
  
      def _make_layer(self, block, planes, blocks, shortcut_type, stride=1):
          downsample = None
          if stride != 1 or self.inplanes != planes * block.expansion:
              if shortcut_type == 'A':
                  downsample = partial(
                      downsample_basic_block,
                      planes=planes * block.expansion,
                      stride=stride)
              else:
                  downsample = nn.Sequential(
                      nn.Conv3d(
                          self.inplanes,
                          planes * block.expansion,
                          kernel_size=1,
                          stride=stride,
                          bias=False), nn.BatchNorm3d(planes * block.expansion))
  
          layers = []
          layers.append(block(self.inplanes, planes, stride, downsample))
          self.inplanes = planes * block.expansion
          for i in range(1, blocks):
              layers.append(block(self.inplanes, planes))
  
          return nn.Sequential(*layers)
  
      def forward(self, x):
          x = self.conv1(x)
          x = self.bn1(x)
          x = self.relu(x)
          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)
  
          return x
  
  
  def get_fine_tuning_parameters(model, ft_portion):
      if ft_portion == "complete":
          return model.parameters()
  
      elif ft_portion == "last_layer":
          ft_module_names = []
          ft_module_names.append('classifier')
  
          parameters = []
          for k, v in model.named_parameters():
              for ft_module in ft_module_names:
                  if ft_module in k:
                      parameters.append({'params': v})
                      break
              else:
                  parameters.append({'params': v, 'lr': 0.0})
          return parameters
  
      else:
          raise ValueError("Unsupported ft_portion: 'complete' or 'last_layer' expected")
  
  
  def resnet10(**kwargs):
      """Constructs a ResNet-10 model.
      """
      model = ResNet(BasicBlock, [1, 1, 1, 1], **kwargs)
      return model
  
  
  def resnet18(**kwargs):
      """Constructs a ResNet-18 model.
      """
      model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
      return model
  
  
  def resnet34(**kwargs):
      """Constructs a ResNet-34 model.
      """
      model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
      return model
  
  
  def resnet50(**kwargs):
      """Constructs a ResNet-50 model.
      """
      model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
      return model
  
  
  def resnet101(**kwargs):
      """Constructs a ResNet-101 model.
      """
      model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
      return model
  
  
  def resnet152(**kwargs):
      """Constructs a ResNet-101 model.
      """
      model = ResNet(Bottleneck, [3, 8, 36, 3], **kwargs)
      return model
  
  
  def resnet200(**kwargs):
      """Constructs a ResNet-101 model.
      """
      model = ResNet(Bottleneck, [3, 24, 36, 3], **kwargs)
      return model
  
apis: torch.nn.init.kaiming_normal torch.nn.MaxPool3d torch.nn.BatchNorm3d torch.nn.Linear torch.nn.Sequential torch.nn.AvgPool3d torch.nn.Conv3d torch.nn.functional.avg_pool3d torch.nn.ReLU torch.cat