class_docstr: input:
   x: B x C x H x W  (C = 2048)
  output: B x nClass x H x W
class_docstr_tok: ["input",":","x",":","B","x","C","x","H","x","W","(","C","=","2048",")","output",":","B","x","nClass","x","H","x","W"]
code: def _make_level(inChannel, outChannel, r1, r2, norm_layer):
      avg_agg = nn.AvgPool2d(r2, stride=1, padding=r2 // 2)
      conv = nn.Sequential(
          nn.Conv2d(
              inChannel,
              outChannel,
              kernel_size=3,
              stride=1,
              padding=r1,
              dilation=r1,
          ),
          norm_layer(outChannel),
          nn.ReLU(),
      )
      return nn.Sequential(avg_agg, conv)
  
file_path: pywick/pywick/models/segmentation/testnets/tkcnet/tkcnet.py
imports: import torch
  import torch.nn
  from torch.nn.functional import upsample
  from .base import BaseNet
label_desc: Use image node_id 2 for calling the TFAHead obj's underlying member method code with example usage: obj._make_level(inChannel, outChannel, r1, r2, norm_layer) and returns: nn