class_docstr: NAS-FPN.
  
  Implementation of `NAS-FPN: Learning Scalable Feature Pyramid Architecture
  for Object Detection <https://arxiv.org/abs/1904.07392>`_
  
  Args:
      in_channels (List[int]): Number of input channels per scale.
      out_channels (int): Number of output channels (used at each scale)
      num_outs (int): Number of output scales.
      stack_times (int): The number of times the pyramid architecture will
          be stacked.
      start_level (int): Index of the start input backbone level used to
          build the feature pyramid. Default: 0.
      end_level (int): Index of the end input backbone level (exclusive) to
          build the feature pyramid. Default: -1, which means the last level.
      add_extra_convs (bool): It decides whether to add conv
          layers on top of the original feature maps. Default to False.
          If True, its actual mode is specified by `extra_convs_on_inputs`.
docstr: Initialize the weights of module.
docstr_tok: ["Initialize","the","weights","of","module","."]
code: def init_weights(self):
      """Initialize the weights of module."""
      for m in self.modules():
          if isinstance(m, nn.Conv2d):
              caffe2_xavier_init(m)
  
file_path: insightface/detection/scrfd/mmdet/models/necks/nas_fpn.py
imports: import torch.nn
  from mmcv.cnn import ConvModule, caffe2_xavier_init
  from mmcv.ops.merge_cells import GlobalPoolingCell, SumCell
  from ..builder import NECKS
label_desc: Use image node_id 2 for calling the NASFPN obj's underlying member method code with example usage: obj.init_weights() without return types