code: def _xavier_init(conv: nn.Module):
      for layer in conv.modules():
          if isinstance(layer, nn.Conv2d):
              torch.nn.init.xavier_uniform_(layer.weight)
              if layer.bias is not None:
                  torch.nn.init.constant_(layer.bias, 0.0)
  
imports: import warnings
  from collections import OrderedDict
  from typing import Any, Dict, List, Optional, Tuple
  import torch
  import torch.nn.functional
  from torch import nn, Tensor
  from ...ops import boxes
  from ...transforms._presets import ObjectDetection
  from ...utils import _log_api_usage_once
  from .._api import register_model, Weights, WeightsEnum
  from .._meta import _COCO_CATEGORIES
  from .._utils import _ovewrite_value_param, handle_legacy_interface
  from ..vgg import VGG, vgg16, VGG16_Weights
  from .None import _utils
  from .anchor_utils import DefaultBoxGenerator
  from .backbone_utils import _validate_trainable_layers
  from .transform import GeneralizedRCNNTransform
label_desc: Use image node_id 1 for calling a global function with example usage: _xavier_init(conv) without return types