class_docstr: Reduced ResNet 32
  
  This network architecture is an adaptation of ResNet for smaller
  input size. The number of blocks can be modified in this line by
  changing n=5 to n=3 for ResNet-20, and n=9 for ResNet-56.
  
  Implementation based on FACIL, as in:
  https://github.com/mmasana/FACIL/blob/master/src/networks/resnet32.py
class_docstr_tok: ["Reduced","ResNet","32","This","network","architecture","is","an","adaptation","of","ResNet","for","smaller","input","size",".","The","number","of","blocks","can","be","modified","in","this","line","by","changing","n=5","to","n=3","for","ResNet-20",",","and","n=9","for","ResNet-56",".","Implementation","based","on","FACIL",",","as","in",":","https",":","\/\/github.com\/mmasana\/FACIL\/blob\/master\/src\/networks\/resnet32.py"]
code: def _make_layer(self, block, planes, blocks, stride=1):
      downsample = None
      if stride != 1 or self.inplanes != planes * block.expansion:
          downsample = nn.Sequential(
              nn.Conv2d(
                  self.inplanes,
                  planes * block.expansion,
                  kernel_size=1,
                  stride=stride,
                  bias=False,
              ),
              nn.BatchNorm2d(planes * block.expansion),
          )
      layers = []
      layers.append(block(self.inplanes, planes, stride, downsample))
      self.inplanes = planes * block.expansion
      for i in range(1, blocks):
          layers.append(block(self.inplanes, planes))
      return nn.Sequential(*layers)
  
file_path: avalanche/avalanche/models/resnet32.py
imports: import torch.nn
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, blocks, stride) and returns: nn