code: def __init__(self, block, n_size, num_classes=10, reduction=16):
      super(CifarSEResNet, self).__init__()
      self.inplane = 16
      self.conv1 = nn.Conv2d(
          3,
          self.inplane,
          kernel_size=3,
          stride=1,
          padding=1,
          bias=False,
      )
      self.bn1 = nn.BatchNorm2d(self.inplane)
      self.relu = nn.ReLU(inplace=True)
      self.layer1 = self._make_layer(
          block, 16, blocks=n_size, stride=1, reduction=reduction
      )
      self.layer2 = self._make_layer(
          block, 32, blocks=n_size, stride=2, reduction=reduction
      )
      self.layer3 = self._make_layer(
          block, 64, blocks=n_size, stride=2, reduction=reduction
      )
      self.avgpool = nn.AdaptiveAvgPool2d(1)
      self.fc = nn.Linear(64, num_classes)
      self.initialize()
  
imports: import numpy
  import torch
  from torch import nn
  from torch.autograd import Variable
  from torchvision.models import ResNet
  from pytorch2keras.converter import pytorch_to_keras
label_desc: Use image node_id 1 to create a new CifarSEResNet object from inherited base classes: nn with example: obj = CifarSEResNet(block, n_size, num_classes, reduction)