code: def weights_init(layers):
      for layer in layers:
          classname = layer.__class__.__name__
          if hasattr(layer, "weight") and (
              classname.find("Conv") != -1
              or classname.find("Linear") != -1
          ):
              layer.weight.set_data(
                  nd.random.normal(
                      0.0, 0.02, shape=layer.weight.data().shape
                  )
              )
              if hasattr(layer, "bias") and layer.bias is not None:
                  layer.bias.set_data(nd.zeros(layer.bias.data().shape))
          elif classname.find("BatchNorm") != -1:
              layer.gamma.set_data(
                  nd.random.normal(
                      1.0, 0.02, shape=layer.gamma.data().shape
                  )
              )
              layer.beta.set_data(nd.zeros(layer.bias.data().shape))
  
imports: import argparse
  import random
  from mxnet import gluon, image, autograd
  from mxnet.gluon.data.vision import transforms
  from mxnet.base import numeric_types
  import gluoncv
  from gluoncv import utils
  from mxnet.gluon.data import DataLoader
  from mxnet.gluon import nn
  import mxnet.ndarray
  import mxnet
  import os
  import numpy
  from mxboard import SummaryWriter
label_desc: Use image node_id 3 for calling a global function with example usage: weights_init(layers) without return types