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
  
file_path: pytorch-image-models/timm/layers/cond_conv2d.py
imports: import math
  from functools import partial
  import numpy
  import torch
  from torch import nn
  from torch.nn import functional
  from .helpers import to_2tuple
  from .conv2d_same import conv2d_same
  from .padding import get_padding_value
label_desc: Use image node_id 1 for calling a global function with example usage: get_condconv_initializer(initializer, num_experts, expert_shape) and returns: condconv_initializer