code: def __init__(self, in_planes, planes, stride=1):
      super(BasicBlock, self).__init__()
      self.conv1 = conv3x3(in_planes, planes, stride)
      self.bn1 = nn.BatchNorm2d(planes)
      self.conv2 = conv3x3(planes, planes)
      self.bn2 = nn.BatchNorm2d(planes)
  
      self.shortcut = nn.Sequential()
      if stride != 1 or in_planes != self.expansion * planes:
          self.shortcut = nn.Sequential(
              nn.Conv2d(
                  in_planes,
                  self.expansion * planes,
                  kernel_size=1,
                  stride=stride,
                  bias=False,
              ),
              nn.BatchNorm2d(self.expansion * planes),
          )
  
imports: import torch.nn
  from torch.nn.functional import relu, avg_pool2d
  from avalanche.models import MultiHeadClassifier, MultiTaskModule, DynamicModule
label_desc: Use image node_id 1 to create a new BasicBlock object from inherited base classes: nn with example: obj = BasicBlock(in_planes, planes, stride)