code: def _init_weights(self, m):
      if isinstance(m, nn.Linear):
          torch.nn.init.trunc_normal_(m.weight, std=0.02)
          if isinstance(m, nn.Linear) and m.bias is not None:
              nn.init.constant_(m.bias, 0)
      elif isinstance(m, nn.LayerNorm):
          nn.init.constant_(m.bias, 0)
          nn.init.constant_(m.weight, 1.0)
      elif isinstance(m, nn.Conv2d):
          fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
          fan_out //= m.groups
          m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
          if m.bias is not None:
              m.bias.data.zero_()
  
imports: import math
  from functools import partial
  from typing import Optional
  import torch
  import torch.nn
  from pytorch_toolbelt.modules.interfaces import AbstractEncoder, FeatureMapsSpecification
  from pytorch_toolbelt.modules.activations import instantiate_activation_block, ACT_GELU
  from pytorch_toolbelt.modules.drop_path import DropPath
label_desc: Use image node_id 4 for calling the MixVisionTransformer obj's underlying member method code with example usage: obj._init_weights(m) without return types