text: import torch as t
  import numpy as np
  import torch.nn as nn
  from torch.nn.utils.weight_norm import weight_norm
  
  from .base import NeuralNetworkModule
  
  
  def conv1x1(in_planes, out_planes, stride=1):
      """
      Create a 1x1 2d convolution block
      """
      return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
  
  
  def conv3x3(in_planes, out_planes, stride=1):
      """
      Create a 3x3 2d convolution block
      """
      return nn.Conv2d(
          in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False
      )
  
  
  def conv5x5(in_planes, out_planes, stride=2):
      """
      Create a 5x5 2d convolution block
      """
      return nn.Conv2d(
          in_planes, out_planes, kernel_size=5, stride=stride, padding=2, bias=False
      )
  
  
  def none_norm(*_, **__):
      return nn.Sequential()
  
  
  def cfg(depth, norm="none"):
      depth_lst = [18, 34, 50, 101, 152]
      if depth not in depth_lst:
          raise ValueError(
              "Error : Resnet depth should be either " "18, 34, 50, 101, 152"
          )
      if norm == "batch":
          cfg_dict = {
              "18": (BasicBlock, [2, 2, 2, 2], {}),
              "34": (BasicBlock, [3, 4, 6, 3], {}),
              "50": (Bottleneck, [3, 4, 6, 3], {}),
              "101": (Bottleneck, [3, 4, 23, 3], {}),
              "152": (Bottleneck, [3, 8, 36, 3], {}),
          }
      elif norm == "weight":
          cfg_dict = {
              "18": (BasicBlockWN, [2, 2, 2, 2], {}),
              "34": (BasicBlockWN, [3, 4, 6, 3], {}),
              "50": (BottleneckWN, [3, 4, 6, 3], {}),
              "101": (BottleneckWN, [3, 4, 23, 3], {}),
              "152": (BottleneckWN, [3, 8, 36, 3], {}),
          }
      elif norm == "none":
          cfg_dict = {
              "18": (BasicBlock, [2, 2, 2, 2], {"norm_layer": none_norm}),
              "34": (BasicBlock, [3, 4, 6, 3], {"norm_layer": none_norm}),
              "50": (Bottleneck, [3, 4, 6, 3], {"norm_layer": none_norm}),
              "101": (Bottleneck, [3, 4, 23, 3], {"norm_layer": none_norm}),
              "152": (Bottleneck, [3, 8, 36, 3], {"norm_layer": none_norm}),
          }
      else:
          raise ValueError(f'Invalid normalization method: "{norm}"')
      return cfg_dict[str(depth)]
  
  
  class BasicBlock(NeuralNetworkModule):
      # Expansion parameter, output will have "expansion * in_planes" depth.
      expansion = 1
  
      def __init__(self, in_planes, out_planes, stride=1, norm_layer=None, **__):
          """
          Create a basic block of resnet.
  
          Args:
              in_planes:  Number of input planes.
              out_planes: Number of output planes.
              stride:     Stride of convolution.
          """
          if norm_layer is None:
              norm_layer = nn.BatchNorm2d
          super().__init__()
          self.conv1 = conv3x3(in_planes, out_planes, stride)
          self.bn1 = norm_layer(out_planes)
          self.conv2 = conv3x3(out_planes, self.expansion * out_planes)
          self.bn2 = norm_layer(self.expansion * out_planes)
          # Create a shortcut from input to output.
          # An empty sequential structure means no transformation
          # is made on input X.
          self.shortcut = nn.Sequential()
  
          self.set_input_module(self.conv1)
  
          # A convolution is needed if we cannot directly add input X to output
          # BatchNorm2d produces NaN gradient?
          if stride != 1 or in_planes != self.expansion * out_planes:
              self.shortcut = nn.Sequential(
                  conv1x1(in_planes, self.expansion * out_planes, stride),
                  norm_layer(self.expansion * out_planes),
              )
  
      def forward(self, x):
          out = t.relu(self.bn1(self.conv1(x)))
          out = self.bn2(self.conv2(out))
          out += self.shortcut(x)
          out = t.relu(out)
          return out
  
  
  class Bottleneck(NeuralNetworkModule):
      # Expansion parameter, output will have "expansion * in_planes" depth.
      expansion = 4
  
      def __init__(self, in_planes, out_planes, stride=1, norm_layer=None, **__):
          """
          Create a bottleneck block of resnet.
  
          Args:
              in_planes:  Number of input planes.
              out_planes: Number of output planes.
              stride:     Stride of convolution.
          """
          if norm_layer is None:
              norm_layer = nn.BatchNorm2d
          super().__init__()
          self.conv1 = conv1x1(in_planes, out_planes)
          self.conv2 = conv3x3(out_planes, out_planes, stride=stride)
          self.conv3 = conv1x1(out_planes, self.expansion * out_planes)
          self.bn1 = norm_layer(out_planes)
          self.bn2 = norm_layer(out_planes)
          self.bn3 = norm_layer(self.expansion * out_planes)
  
          self.shortcut = nn.Sequential()
  
          self.set_input_module(self.conv1)
  
          if stride != 1 or in_planes != self.expansion * out_planes:
              self.shortcut = nn.Sequential(
                  conv1x1(in_planes, self.expansion * out_planes, stride),
                  norm_layer(self.expansion * out_planes),
              )
  
      def forward(self, x):
          out = t.relu(self.bn1(self.conv1(x)))
          out = t.relu(self.bn2(self.conv2(out)))
          out = self.bn3(self.conv3(out))
          out += self.shortcut(x)
          out = t.relu(out)
          return out
  
  
  class BasicBlockWN(NeuralNetworkModule):
      """
      Basic block with weight normalization
      """
  
      # Expansion parameter, output will have "expansion * in_planes" depth.
      expansion = 1
  
      def __init__(self, in_planes, out_planes, stride=1, **__):
          """
          Create a basic block of resnet.
  
          Args:
              in_planes:  Number of input planes.
              out_planes: Number of output planes.
              stride:     Stride of convolution.
          """
          super().__init__()
          self.conv1 = weight_norm(conv3x3(in_planes, out_planes, stride))
          self.conv2 = weight_norm(conv3x3(out_planes, self.expansion * out_planes))
          # Create a shortcut from input to output.
          # An empty sequential structure means no transformation
          # is made on input X.
          self.shortcut = nn.Sequential()
  
          self.set_input_module(self.conv1)
  
          # A convolution is needed if we cannot directly add input X to output.
          if stride != 1 or in_planes != self.expansion * out_planes:
              self.shortcut = nn.Sequential(
                  weight_norm(conv1x1(in_planes, self.expansion * out_planes, stride)),
              )
  
      def forward(self, x):
          out = t.relu(self.conv1(x))
          out = self.conv2(out)
          out += self.shortcut(x)
          out = t.relu(out)
          return out
  
  
  class BottleneckWN(NeuralNetworkModule):
      """
      Bottleneck block with weight normalization
      """
  
      # expansion parameter, output will have "expansion * in_planes" depth
      expansion = 4
  
      def __init__(self, in_planes, out_planes, stride=1, **__):
          """
          Create a bottleneck block of resnet.
  
          Args:
              in_planes:  Number of input planes.
              out_planes: Number of output planes.
              stride:     Stride of convolution.
          """
          super().__init__()
          self.conv1 = weight_norm(
              nn.Conv2d(in_planes, out_planes, kernel_size=1, bias=False)
          )
          self.conv2 = weight_norm(
              nn.Conv2d(
                  out_planes,
                  out_planes,
                  kernel_size=3,
                  stride=stride,
                  padding=1,
                  bias=False,
              )
          )
          self.conv3 = weight_norm(
              nn.Conv2d(
                  out_planes, self.expansion * out_planes, kernel_size=1, bias=False
              )
          )
  
          self.shortcut = nn.Sequential()
  
          self.set_input_module(self.conv1)
  
          if stride != 1 or in_planes != self.expansion * out_planes:
              self.shortcut = nn.Sequential(
                  weight_norm(conv1x1(in_planes, self.expansion * out_planes, stride)),
              )
  
      def forward(self, x):
          out = t.relu(self.conv1(x))
          out = t.relu(self.conv2(out))
          out = self.conv3(out)
          out += self.shortcut(x)
          out = t.relu(out)
  
          return out
  
  
  class ResNet(NeuralNetworkModule):
      def __init__(
          self,
          in_planes: int,
          depth: int,
          out_planes: int,
          out_pool_size=(1, 1),
          norm="none",
      ):
          """
          Create a resnet of specified depth.
  
          Args:
              in_planes:  Number of input planes.
              depth: Depth of resnet. Could be one of ``18, 34, 50, 101, 152``.
              out_planes: Number of output planes.
              out_pool_size: Size of pooling output
              norm: Normalization method, could be one of "none", "batch" or
                  "weight".
          """
          super().__init__()
          self.norm = norm
          self.depth = depth
          self.in_planes = in_planes
          self.out_planes = out_planes
          self.out_pool_size = out_pool_size
  
          self._cur_in_planes = 64
  
          block, num_blocks, kw = cfg(depth, norm)
  
          self.conv1 = nn.Conv2d(
              in_planes, 64, kernel_size=7, stride=2, padding=3, bias=False
          )
          self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
          if norm == "batch":
              self.bn1 = nn.BatchNorm2d(64)
              self.layer1 = self._make_layer(block, 64, num_blocks[0], 1, kw)
              self.layer2 = self._make_layer(block, 128, num_blocks[1], 2, kw)
              self.layer3 = self._make_layer(block, 256, num_blocks[2], 2, kw)
              self.layer4 = self._make_layer(block, 512, num_blocks[3], 2, kw)
              self.base = nn.Sequential(
                  self.conv1,
                  self.bn1,
                  nn.ReLU(),
                  self.maxpool,
                  self.layer1,
                  self.layer2,
                  self.layer3,
                  self.layer4,
              )
          else:
              self.layer1 = self._make_layer(block, 64, num_blocks[0], 1, kw)
              self.layer2 = self._make_layer(block, 128, num_blocks[1], 2, kw)
              self.layer3 = self._make_layer(block, 256, num_blocks[2], 2, kw)
              self.layer4 = self._make_layer(block, 512, num_blocks[3], 2, kw)
              self.base = nn.Sequential(
                  self.conv1,
                  nn.ReLU(),
                  self.maxpool,
                  self.layer1,
                  self.layer2,
                  self.layer3,
                  self.layer4,
              )
          self.fc = nn.Linear(512 * out_pool_size[0] * out_pool_size[1], out_planes)
  
          self.set_input_module(self.conv1)
  
      def _make_layer(self, block, planes, num_blocks, stride, kwargs):
          strides = [stride] + [1] * (num_blocks - 1)
          layers = []
  
          for stride in strides:
              layers.append(block(self._cur_in_planes, planes, stride, **kwargs))
              self._cur_in_planes = planes * block.expansion
  
          return nn.Sequential(*layers)
  
      def forward(self, x):
          assert x.shape[2] == 224 and x.shape[3] == 224
          x = self.base(x)
          kernel_size = (
              np.int(np.floor(x.size(2) / self.out_pool_size[0])),
              np.int(np.floor(x.size(3) / self.out_pool_size[1])),
          )
          x = nn.functional.avg_pool2d(x, kernel_size)
          x = x.view(x.size(0), -1)
          x = self.fc(x)
          return x
  