code: def _compute_and_retrieve_margin_of_error(variant_weight):
      """
      Computes the margin of error using the Wald method for computing the confidence
      intervals of a binomial distribution.
      """
      z_value = st.norm.ppf(
          DESIRED_CONFIDENCE_FOR_VARIANT_TRAFFIC_DISTRIBUTION
      )
      margin_of_error = (
          variant_weight * (1 - variant_weight)
      ) / VARIANT_TRAFFIC_SAMPLING_COUNT
      margin_of_error = z_value * math.sqrt(margin_of_error)
      return margin_of_error
  
imports: from __future__ import absolute_import
  import json
  import math
  import os
  import pytest
  import scipy.stats
  from sagemaker import image_uris
  from sagemaker.deserializers import CSVDeserializer
  from sagemaker.s3 import S3Uploader
  from sagemaker.session import production_variant
  from sagemaker.sparkml import SparkMLModel
  from sagemaker.utils import unique_name_from_base
  from sagemaker.predictor import Predictor
  from sagemaker.serializers import CSVSerializer
  import tests.integ
label_desc: Use image node_id 8 for calling a global function with example usage: _compute_and_retrieve_margin_of_error(variant_weight) and returns: margin_of_error