code: def __init__(
      self, in_planes: int, planes: int, stride: int = 1
  ) -> None:
      super().__init__()
      self.conv1 = conv3x3(in_planes, planes, stride)
      self.conv2 = conv3x3(planes, planes)
      self.bn1 = nn.BatchNorm2d(planes)
      self.bn2 = nn.BatchNorm2d(planes)
      self.relu = nn.ReLU(inplace=True)
  
      if stride == 1:
          self.downsample = None
      else:
          self.downsample = nn.Sequential(
              conv1x1(in_planes, planes, stride=stride),
              nn.BatchNorm2d(planes),
          )
  
file_path: kornia/kornia/feature/loftr/backbone/resnet_fpn.py
imports: from typing import Any, Dict, List, Type, Union
  import torch.nn.functional
  from torch import nn
  from kornia.core import Module, Tensor
label_desc: Use image node_id 1 to create a new BasicBlock object from inherited base classes: Module with example: obj = BasicBlock(in_planes, planes, stride)