code: def plot_discrete_cdf(
      xs, ys, ax=None, 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
      ----------
  
      xs : list-like of scalars
          x values corresponding to the values in `y`s. Can be `None`, in which
          case range(len(ys)) will be used.
  
      ys : list-like of scalars
          list of probabilities to be plotted which should sum to 1.
  
      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()
  
      if xs is None:
          xs = range(len(ys))
      ys = np.cumsum(ys)
      ax.plot(xs, ys, label=label)
      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 12 for calling a global function with example usage: plot_discrete_cdf(xs, ys, ax, xlabel, ylabel, label) and returns: ax