docstr: Initialize the weights.
docstr_tok: ["Initialize","the","weights","."]
code: def init_weights(self) -> None:
      """Initialize the weights."""
      super().init_weights()
      for m in [self.upsample, self.conv_logits]:
          if m is None:
              continue
          elif isinstance(m, CARAFEPack):
              m.init_weights()
          elif hasattr(m, "weight") and hasattr(m, "bias"):
              nn.init.kaiming_normal_(
                  m.weight, mode="fan_out", nonlinearity="relu"
              )
              nn.init.constant_(m.bias, 0)
  
imports: from typing import List, Tuple
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from mmcv.cnn import ConvModule, build_conv_layer, build_upsample_layer
  from mmcv.ops.carafe import CARAFEPack
  from mmengine.config import ConfigDict
  from mmengine.model import BaseModule, ModuleList
  from mmengine.structures import InstanceData
  from torch import Tensor
  from torch.nn.modules.utils import _pair
  from mmdet.models.task_modules.samplers import SamplingResult
  from mmdet.models.utils import empty_instances
  from mmdet.registry import MODELS
  from mmdet.structures.mask import mask_target
  from mmdet.utils import ConfigType, InstanceList, OptConfigType, OptMultiConfig
label_desc: Use image node_id 2 for calling the FCNMaskHead obj's underlying member method code with example usage: obj.init_weights() without return types