code: def initialize(module, init_cfg):
      """Initialize a module.
  
      Args:
          module (``torch.nn.Module``): the module will be initialized.
          init_cfg (dict | list[dict]): initialization configuration dict to
              define initializer. OpenMMLab has implemented 6 initializers
              including ``Constant``, ``Xavier``, ``Normal``, ``Uniform``,
              ``Kaiming``, and ``Pretrained``.
      Example:
          >>> module = nn.Linear(2, 3, bias=True)
          >>> init_cfg = dict(type='Constant', layer='Linear', val =1 , bias =2)
          >>> initialize(module, init_cfg)
  
          >>> module = nn.Sequential(nn.Conv1d(3, 1, 3), nn.Linear(1,2))
          >>> # define key ``'layer'`` for initializing layer with different
          >>> # configuration
          >>> init_cfg = [dict(type='Constant', layer='Conv1d', val=1),
                  dict(type='Constant', layer='Linear', val=2)]
          >>> initialize(module, init_cfg)
  
          >>> # define key``'override'`` to initialize some specific part in
          >>> # module
          >>> class FooNet(nn.Module):
          >>>     def __init__(self):
          >>>         super().__init__()
          >>>         self.feat = nn.Conv2d(3, 16, 3)
          >>>         self.reg = nn.Conv2d(16, 10, 3)
          >>>         self.cls = nn.Conv2d(16, 5, 3)
          >>> model = FooNet()
          >>> init_cfg = dict(type='Constant', val=1, bias=2, layer='Conv2d',
          >>>     override=dict(type='Constant', name='reg', val=3, bias=4))
          >>> initialize(model, init_cfg)
  
          >>> model = ResNet(depth=50)
          >>> # Initialize weights with the pretrained model.
          >>> init_cfg = dict(type='Pretrained',
                  checkpoint='torchvision://resnet50')
          >>> initialize(model, init_cfg)
  
          >>> # Initialize weights of a sub-module with the specific part of
          >>> # a pretrained model by using "prefix".
          >>> url = 'http://download.openmmlab.com/mmdetection/v2.0/retinanet/'\
          >>>     'retinanet_r50_fpn_1x_coco/'\
          >>>     'retinanet_r50_fpn_1x_coco_20200130-c2398f9e.pth'
          >>> init_cfg = dict(type='Pretrained',
                  checkpoint=url, prefix='backbone.')
      """
      if not isinstance(init_cfg, (dict, list)):
          raise TypeError(
              f"init_cfg must be a dict or a list of dict, \
                  but got {type(init_cfg)}"
          )
  
      if isinstance(init_cfg, dict):
          init_cfg = [init_cfg]
  
      for cfg in init_cfg:
          # should deeply copy the original config because cfg may be used by
          # other modules, e.g., one init_cfg shared by multiple bottleneck
          # blocks, the expected cfg will be changed after pop and will change
          # the initialization behavior of other modules
          cp_cfg = copy.deepcopy(cfg)
          override = cp_cfg.pop("override", None)
          _initialize(module, cp_cfg)
  
          if override is not None:
              cp_cfg.pop("layer", None)
              _initialize_override(module, override, cp_cfg)
          else:
              # All attributes in module have same initialization.
              pass
  
imports: import copy
  import math
  import warnings
  import numpy
  import torch
  import torch.nn
  from torch import Tensor
  from annotator.uniformer.mmcv.utils import Registry, build_from_cfg, get_logger, print_log
label_desc: Use image node_id 13 for calling a global function with example usage: initialize(module, init_cfg) without return types