code: def _init_weights(self, m):
      try:
          from apex.normalization import FusedLayerNorm
  
          fn = FusedLayerNorm
      except:
          fn = nn.LayerNorm
  
      if isinstance(m, nn.Linear):
          torch.nn.init.xavier_uniform_(m.weight)
          if isinstance(m, nn.Linear) and m.bias is not None:
              nn.init.constant_(m.bias, 0)
      elif isinstance(m, nn.LayerNorm) or isinstance(m, fn):
          if m.bias is not None:
              nn.init.constant_(m.bias, 0)
          if m.weight is not None:
              nn.init.constant_(m.weight, 1.0)
  
imports: import logging
  import math
  from dataclasses import dataclass, field
  from typing import Optional, Callable
  from functools import partial
  import numpy
  from omegaconf import II
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.distributed
  from fairseq.modules import EMAModule, EMAModuleConfig
  from fairseq.dataclass import FairseqDataclass
  from fairseq.models import BaseFairseqModel, register_model
  from examples.data2vec.data.modality import Modality
  from examples.data2vec.models.modalities.base import MaskSeed, D2vModalityConfig, ModalitySpecificEncoder, get_annealed_rate
  from examples.data2vec.models.modalities.modules import D2vDecoderConfig, AltBlock, Decoder1d
  from examples.data2vec.models.modalities.audio import D2vAudioConfig, AudioEncoder
  from examples.data2vec.models.modalities.images import D2vImageConfig, ImageEncoder
  from examples.data2vec.models.modalities.text import D2vTextConfig, TextEncoder
label_desc: Use image node_id 3 for calling the Data2VecMultiModel obj's underlying member method code with example usage: obj._init_weights(m) without return types