code: def _make_stack_3x3_convs(
      num_convs,
      in_channels,
      out_channels,
      act_cfg=dict(type="ReLU", inplace=True),
  ):
      convs = []
      for _ in range(num_convs):
          convs.append(
              nn.Conv2d(in_channels, out_channels, 3, padding=1)
          )
          convs.append(MODELS.build(act_cfg))
          in_channels = out_channels
      return nn.Sequential(*convs)
  
imports: import math
  import torch
  import torch.nn
  import torch.nn.functional
  from mmengine.model.weight_init import caffe2_xavier_init, kaiming_init
  from torch.nn import init
  from mmdet.registry import MODELS
label_desc: Use image node_id 1 for calling a global function with example usage: _make_stack_3x3_convs(num_convs, in_channels, out_channels, act_cfg) and returns: nn