class_docstr: Initializes weights with random values sampled from a normal distribution
  with a custom mean and standard deviation of `sigma`.
class_docstr_tok: ["Initializes","weights","with","random","values","sampled","from","a","normal","distribution","with","a","custom","mean","and","standard","deviation","of","`","sigma","`","."]
code: def __init__(self, mean=0, sigma=0.01, bounded=False):
      super(CustomNormalInit, self).__init__(
          mean=mean, sigma=sigma, bounded=bounded
      )
      self.mean = mean
      self.sigma = sigma
      self.bounded = bounded
  
imports: import mxnet
  import numpy
  import unittest
  import pytest
  import ctypes
  import copy
  from mxnet.contrib import quantization
  from mxnet.gluon import nn
  from mxnet.test_utils import assert_almost_equal, assert_almost_equal_with_err
label_desc: Use image node_id 1 to create a new CustomNormalInit object from inherited base classes: mx.init with example: obj = CustomNormalInit(mean, sigma, bounded)