code: def __init__(self, inplanes, planes, stride=1, reduction=16):
      super(CifarSEBasicBlock, self).__init__()
      self.conv1 = conv3x3(inplanes, planes, stride)
      self.bn1 = nn.BatchNorm2d(planes)
      self.relu = nn.ReLU(inplace=True)
      self.conv2 = conv3x3(planes, planes)
      self.bn2 = nn.BatchNorm2d(planes)
      self.se = SELayer(planes, reduction)
      if inplanes != planes:
          self.downsample = nn.Sequential(
              nn.Conv2d(
                  inplanes,
                  planes,
                  kernel_size=1,
                  stride=stride,
                  bias=False,
              ),
              nn.BatchNorm2d(planes),
          )
      else:
          self.downsample = lambda x: x
      self.stride = stride
  
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 CifarSEBasicBlock object from inherited base classes: nn with example: obj = CifarSEBasicBlock(inplanes, planes, stride, reduction)