class_docstr: This class implements the `Feature Pyramid Network <https://arxiv.org/abs/1612.03144>`_ module for object detection.
  
  Args:
      opts: command-line arguments
      in_channels (List[int]): List of channels at different output strides
      output_strides (List[int]): Feature maps from these output strides will be used in FPN
      out_channels (int): Output channels
docstr: Resets the weights of FPN layers
docstr_tok: ["Resets","the","weights","of","FPN","layers"]
code: def reset_weights(self) -> None:
      """Resets the weights of FPN layers"""
      for m in self.modules():
          if isinstance(m, nn.Conv2d):
              initialize_conv_layer(m, init_method="xavier_uniform")
          elif isinstance(m, norm_layers_tuple):
              initialize_norm_layers(m)
  
file_path: ml-cvnets/cvnets/modules/feature_pyramid.py
imports: from typing import Dict, List
  import torch
  import torch.nn.functional
  from torch import Tensor, nn
  from cvnets.layers import ConvLayer2d, norm_layers_tuple
  from cvnets.misc.init_utils import initialize_conv_layer, initialize_norm_layers
  from cvnets.modules import BaseModule
  from utils import logger
label_desc: Use image node_id 2 for calling the FeaturePyramidNetwork obj's underlying member method code with example usage: obj.reset_weights() without return types