code: def __init__(
      self,
      block,
      layers,
      num_classes=1000,
      zero_init_residual=True,
      groups=8,
      width_per_group=64,
      replace_stride_with_dilation=None,
      norm_layer=None,
      s=0.5,
      **_,
  ):
      super(AxialAttentionNet, self).__init__()
      if norm_layer is None:
          norm_layer = nn.BatchNorm2d
      self._norm_layer = norm_layer
  
      self.inplanes = int(64 * s)
      self.dilation = 1
      if replace_stride_with_dilation is None:
          # each element in the tuple indicates if we should replace
          # the 2x2 stride with a dilated convolution instead
          replace_stride_with_dilation = [False, False, False]
      if len(replace_stride_with_dilation) != 3:
          raise ValueError(
              "replace_stride_with_dilation should be None "
              "or a 3-element tuple, got {}".format(
                  replace_stride_with_dilation
              )
          )
      self.groups = groups
      self.base_width = width_per_group
      self.conv1 = nn.Conv2d(
          3,
          self.inplanes,
          kernel_size=7,
          stride=2,
          padding=3,
          bias=False,
      )
      self.bn1 = norm_layer(self.inplanes)
      self.relu = nn.ReLU(inplace=True)
      self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
      self.layer1 = self._make_layer(
          block, int(128 * s), layers[0], kernel_size=56
      )
      self.layer2 = self._make_layer(
          block,
          int(256 * s),
          layers[1],
          stride=2,
          kernel_size=56,
          dilate=replace_stride_with_dilation[0],
      )
      self.layer3 = self._make_layer(
          block,
          int(512 * s),
          layers[2],
          stride=2,
          kernel_size=28,
          dilate=replace_stride_with_dilation[1],
      )
      self.layer4 = self._make_layer(
          block,
          int(1024 * s),
          layers[3],
          stride=2,
          kernel_size=14,
          dilate=replace_stride_with_dilation[2],
      )
      self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
      self.fc = nn.Linear(int(1024 * block.expansion * s), num_classes)
  
      for m in self.modules():
          if isinstance(m, (nn.Conv2d, nn.Conv1d)):
              if isinstance(m, qkv_transform):
                  pass
              else:
                  nn.init.kaiming_normal_(
                      m.weight, mode="fan_out", nonlinearity="relu"
                  )
          elif isinstance(
              m, (nn.BatchNorm2d, nn.BatchNorm1d, nn.GroupNorm)
          ):
              nn.init.constant_(m.weight, 1)
              nn.init.constant_(m.bias, 0)
  
      # Zero-initialize the last BN in each residual branch,
      # so that the residual branch starts with zeros, and each residual block behaves like an identity.
      # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
      if zero_init_residual:
          for m in self.modules():
              if isinstance(m, AxialBlock):
                  nn.init.constant_(m.bn2.weight, 0)
  
imports: import math
  import torch
  import torch.nn
  import torch.nn.functional
label_desc: Use image node_id 1 to create a new AxialAttentionNet object from inherited base classes: nn with example: obj = AxialAttentionNet(block, layers, num_classes, zero_init_residual, groups, width_per_group, replace_stride_with_dilation, norm_layer, s)