code: def _make_stage(self, features, out_features, size):
      prior = nn.AdaptiveAvgPool2d(output_size=(size, size))
      conv = nn.Conv2d(features, out_features, 1)
      return nn.Sequential(prior, conv)
  
file_path: mmdetection/projects/SparseInst/sparseinst/encoder.py
imports: import torch
  import torch.nn
  import torch.nn.functional
  from mmengine.model.weight_init import caffe2_xavier_init, kaiming_init
  from mmdet.registry import MODELS
label_desc: Use image node_id 2 for calling the PyramidPoolingModule obj's underlying member method code with example usage: obj._make_stage(features, out_features, size) and returns: nn