code: def get_condconv_initializer(initializer, num_experts, expert_shape):
      def condconv_initializer(weight):
          """CondConv initializer function."""
          num_params = np.prod(expert_shape)
          if (
              len(weight.shape) != 2
              or weight.shape[0] != num_experts
              or weight.shape[1] != num_params
          ):
              raise (
                  ValueError(
                      "CondConv variables must have shape [num_experts, num_params]"
                  )
              )
          for i in range(num_experts):
              initializer(weight[i].view(expert_shape))
  
      return condconv_initializer
  
imports: import collections.abc
  import math
  from functools import partial
  from itertools import repeat
  from typing import Tuple, Optional
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from .config import *
label_desc: Use image node_id 10 for calling a global function with example usage: get_condconv_initializer(initializer, num_experts, expert_shape) and returns: condconv_initializer