class_docstr: ResNet implementation.
  
  [1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
  Deep Residual Learning for Image Recognition. arXiv:1512.03385
  
  Attributes:
      block:
          ResNet building block type.
      layers:
          List of blocks per layer.
      num_classes:
          Number of classes in final softmax layer.
      width:
          Multiplier for ResNet width.
class_docstr_tok: ["ResNet","implementation",".","[","1","]","Kaiming","He",",","Xiangyu","Zhang",",","Shaoqing","Ren",",","Jian","Sun","Deep","Residual","Learning","for","Image","Recognition",".","arXiv:1512.03385","Attributes",":","block",":","ResNet","building","block","type",".","layers",":","List","of","blocks","per","layer",".","num_classes",":","Number","of","classes","in","final","softmax","layer",".","width",":","Multiplier","for","ResNet","width","."]
code: def _make_layer(
      self,
      block: type[BasicBlock],
      planes: int,
      num_layers: int,
      stride: int,
      num_splits: int,
  ) -> nn.Sequential:
      strides = [stride] + [1] * (num_layers - 1)
      layers = []
      for stride in strides:
          layers.append(
              block(self.in_planes, planes, stride, num_splits)
          )
          self.in_planes = planes * block.expansion
      return nn.Sequential(*layers)
  
imports: from __future__ import annotations
  from typing import List
  import torch.nn
  import torch.nn.functional
  from torch import Tensor
  from lightly.models.batchnorm import get_norm_layer
label_desc: Use image node_id 2 for calling the ResNet obj's underlying member method code with example usage: obj._make_layer(block, planes, num_layers, stride, num_splits) and returns: nn