code: def initialize_weight_goog(m, n="", fix_group_fanout=True):
      # weight init as per Tensorflow Official impl
      # https://github.com/tensorflow/tpu/blob/master/models/official/mnasnet/mnasnet_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: w.data.normal_(0, math.sqrt(2.0 / fan_out)),
              m.num_experts,
              m.weight_shape,
          )
          init_weight_fn(m.weight)
          if m.bias is not None:
              m.bias.data.zero_()
      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
          m.weight.data.normal_(0, math.sqrt(2.0 / fan_out))
          if m.bias is not None:
              m.bias.data.zero_()
      elif isinstance(m, nn.BatchNorm2d):
          m.weight.data.fill_(1.0)
          m.bias.data.zero_()
      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)
          m.weight.data.uniform_(-init_range, init_range)
          m.bias.data.zero_()
  
imports: import re
  from copy import deepcopy
  from .conv2d_layers import *
  from geffnet.activations import *
label_desc: Use image node_id 12 for calling a global function with example usage: initialize_weight_goog(m, n, fix_group_fanout) without return types