code: def plot_gaussian_cdf(
      mean=0.0,
      variance=1.0,
      ax=None,
      xlim=None,
      ylim=(0.0, 1.0),
      xlabel=None,
      ylabel=None,
      label=None,
  ):
      """
      Plots a normal distribution CDF with the given mean and variance.
      x-axis contains the mean, the y-axis shows the cumulative probability.
  
      Parameters
      ----------
  
      mean : scalar, default 0.
          mean for the normal distribution.
  
      variance : scalar, default 0.
          variance for the normal distribution.
  
      ax : matplotlib axes object, optional
          If provided, the axes to draw on, otherwise plt.gca() is used.
  
      xlim, ylim: (float,float), optional
          specify the limits for the x or y axis as tuple (low,high).
          If not specified, limits will be automatically chosen to be 'nice'
  
      xlabel : str,optional
          label for the x-axis
  
      ylabel : str, optional
          label for the y-axis
  
      label : str, optional
          label for the legend
  
      Returns
      -------
          axis of plot
      """
      import matplotlib.pyplot as plt
  
      if ax is None:
          ax = plt.gca()
  
      sigma = math.sqrt(variance)
      n = norm(mean, sigma)
      if xlim is None:
          xlim = [n.ppf(0.001), n.ppf(0.999)]
  
      xs = np.arange(xlim[0], xlim[1], (xlim[1] - xlim[0]) / 1000.0)
      cdf = n.cdf(xs)
      ax.plot(xs, cdf, label=label)
      ax.set_xlim(xlim)
      ax.set_ylim(ylim)
      ax.set_xlabel(xlabel)
      ax.set_ylabel(ylabel)
      return ax
  
imports: from __future__ import absolute_import, division, unicode_literals
  import math
  from math import cos, sin
  import random
  import warnings
  import numpy
  from numpy.linalg import inv
  import scipy.linalg
  import scipy.sparse
  import scipy.sparse.linalg
  from scipy.stats import norm, multivariate_normal
label_desc: Use image node_id 13 for calling a global function with example usage: plot_gaussian_cdf(mean, variance, ax, xlim, ylim, xlabel, ylabel, label) and returns: ax