code: def init_weights(self, nlhb=False):
      head_bias = -math.log(self.num_classes) if nlhb else 0.0
      named_apply(
          partial(_init_weights, head_bias=head_bias), module=self
      )
  
imports: import math
  from functools import partial
  from itertools import accumulate
  from typing import Tuple
  import torch
  import torch.nn
  from timm.data import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, DEFAULT_CROP_PCT
  from timm.layers import lecun_normal_, DropPath, Mlp, PatchEmbed, ClassifierHead
  from ._builder import build_model_with_cfg
  from ._manipulate import named_apply
  from ._registry import register_model, generate_default_cfgs
label_desc: Use image node_id 2 for calling the Sequencer2d obj's underlying member method code with example usage: obj.init_weights(nlhb) without return types