code: def create_parameter(layers, shape, dtype):
      # use layerhelper to init bias, scale, mean, variance
      helper = LayerHelper("batch_norm", **locals())
      param_name = "batch_norm_" + str(layers)
      scale = helper.create_parameter(
          attr=fluid.ParamAttr(name=param_name + ".w" + "_0"),
          shape=[shape],
          dtype=dtype,
          default_initializer=Constant(1.0),
      )
      scale.stop_gradient = True
  
      bias = helper.create_parameter(
          attr=fluid.ParamAttr(name=param_name + ".b" + "_0"),
          shape=[shape],
          dtype=dtype,
          is_bias=True,
      )
      bias.stop_gradient = True
  
      mean = helper.create_parameter(
          attr=ParamAttr(
              name=param_name + ".w" + "_1",
              initializer=Constant(0.0),
              trainable=False,
          ),
          shape=[shape],
          dtype=dtype,
      )
      mean.stop_gradient = True
  
      variance = helper.create_parameter(
          attr=ParamAttr(
              name=param_name + ".w" + "_2",
              initializer=Constant(1.0),
              trainable=False,
          ),
          shape=[shape],
          dtype=dtype,
      )
      variance.stop_gradient = True
  
      return scale, bias, mean, variance
  
imports: from __future__ import division
  from builtins import str
  from builtins import range
  from builtins import object
  from past.utils import old_div
  import paddle
  import paddle.fluid
  import math
  from paddle.fluid.layer_helper import LayerHelper
  from paddle.fluid.param_attr import ParamAttr
  from paddle.fluid.initializer import Constant
label_desc: Use image node_id 1 for calling a global function with example usage: create_parameter(layers, shape, dtype) and returns: scale,  bias,  mean,  variance