code: def _make_layer(
      self, block, planes, blocks, stride=1, dilation=1, multi_grid=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,
              ),
              BatchNorm2d(planes * block.expansion, affine=True),
          )
  
      layers = []
      generate_multi_grid = (
          lambda index, grids: grids[index % len(grids)]
          if isinstance(grids, tuple)
          else 1
      )
      layers.append(
          block(
              self.inplanes,
              planes,
              stride,
              dilation=dilation,
              downsample=downsample,
              multi_grid=generate_multi_grid(0, multi_grid),
          )
      )
      self.inplanes = planes * block.expansion
      for i in range(1, blocks):
          layers.append(
              block(
                  self.inplanes,
                  planes,
                  dilation=dilation,
                  multi_grid=generate_multi_grid(i, multi_grid),
              )
          )
  
      return nn.Sequential(*layers)
  
imports: import torch.nn
  import torch
  import torch.nn.functional
  from torch.nn import BatchNorm2d
label_desc: Use image node_id 2 for calling the GALDNet obj's underlying member method code with example usage: obj._make_layer(block, planes, blocks, stride, dilation, multi_grid) and returns: nn