code: def _init_weight_goog(m, n="", fix_group_fanout=True):
      """Weight initialization as per Tensorflow official implementations.
  
      Args:
          m (nn.Module): module to init
          n (str): module name
          fix_group_fanout (bool): enable correct (matching Tensorflow TPU impl) fanout calculation w/ group convs
  
      Handles layers in EfficientNet, EfficientNet-CondConv, MixNet, MnasNet, MobileNetV3, etc:
      * https://github.com/tensorflow/tpu/blob/master/models/official/mnasnet/mnasnet_model.py
      * https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/efficientnet_model.py
      """
      if isinstance(m, CondConv2d):
          fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
          if fix_group_fanout:
              fan_out //= m.groups
          init_weight_fn = get_condconv_initializer(
              lambda w: nn.init.normal_(w, 0, math.sqrt(2.0 / fan_out)),
              m.num_experts,
              m.weight_shape,
          )
          init_weight_fn(m.weight)
          if m.bias is not None:
              nn.init.zeros_(m.bias)
      elif isinstance(m, nn.Conv2d):
          fan_out = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
          if fix_group_fanout:
              fan_out //= m.groups
          nn.init.normal_(m.weight, 0, math.sqrt(2.0 / fan_out))
          if m.bias is not None:
              nn.init.zeros_(m.bias)
      elif isinstance(m, nn.BatchNorm2d):
          nn.init.ones_(m.weight)
          nn.init.zeros_(m.bias)
      elif isinstance(m, nn.Linear):
          fan_out = m.weight.size(0)  # fan-out
          fan_in = 0
          if "routing_fn" in n:
              fan_in = m.weight.size(1)
          init_range = 1.0 / math.sqrt(fan_in + fan_out)
          nn.init.uniform_(m.weight, -init_range, init_range)
          nn.init.zeros_(m.bias)
  
imports: import logging
  import math
  import re
  from copy import deepcopy
  from functools import partial
  from typing import Any, Dict, List
  import torch.nn
  from ._efficientnet_blocks import *
  from timm.layers import CondConv2d, get_condconv_initializer, get_act_layer, get_attn, make_divisible
label_desc: Use image node_id 10 for calling a global function with example usage: _init_weight_goog(m, n, fix_group_fanout) without return types