class_docstr: ResNet+FPN, output resolution are 1/8 and 1/2.
  
  Each block has 2 layers.
class_docstr_tok: ["ResNet+FPN",",","output","resolution","are","1\/8","and","1\/2",".","Each","block","has","2","layers","."]
code: def _make_layer(
      self,
      block: Union[Type[BasicBlock], Module],
      dim: int,
      stride: int = 1,
  ) -> nn.Sequential:
      layer1 = block(self.in_planes, dim, stride=stride)
      layer2 = block(dim, dim, stride=1)
      layers = (layer1, layer2)
  
      self.in_planes = dim
      return nn.Sequential(*layers)
  
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 2 for calling the ResNetFPN_8_2 obj's underlying member method code with example usage: obj._make_layer(block, dim, stride) and returns: nn