code: def __init__(
      self, inplanes, planes, stride=1, downsample=None, dilation=1
  ):
      super(BasicBlock, self).__init__()
      self.conv1 = conv3x3(
          inplanes, planes, stride=stride, dilation=dilation
      )
      self.bn1 = nn.BatchNorm2d(planes)
      self.relu = nn.ReLU(inplace=True)
      self.conv2 = conv3x3(planes, planes, stride=1, dilation=dilation)
      self.bn2 = nn.BatchNorm2d(planes)
      self.downsample = downsample
      self.stride = stride
  
imports: from collections import OrderedDict
  import math
  import re
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.utils import model_zoo
  from torchvision.models.densenet import DenseNet
  from torchvision.models.squeezenet import squeezenet1_1
label_desc: Use image node_id 1 to create a new BasicBlock object from inherited base classes: nn with example: obj = BasicBlock(inplanes, planes, stride, downsample, dilation)