code: def plot_ic_qq(ic, theoretical_dist=stats.norm, ax=None):
      """
      Plots Spearman Rank Information Coefficient "Q-Q" plot relative to
      a theoretical distribution.
  
      Parameters
      ----------
      ic : pd.DataFrame
          DataFrame indexed by date, with IC for each forward return.
      theoretical_dist : scipy.stats._continuous_distns
          Continuous distribution generator. scipy.stats.norm and
          scipy.stats.t are popular options.
      ax : matplotlib.Axes, optional
          Axes upon which to plot.
  
      Returns
      -------
      ax : matplotlib.Axes
          The axes that were plotted on.
      """
  
      ic = ic.copy()
  
      num_plots = len(ic.columns)
  
      v_spaces = ((num_plots - 1) // 3) + 1
  
      if ax is None:
          f, ax = plt.subplots(v_spaces, 3, figsize=(18, v_spaces * 6))
          ax = ax.flatten()
  
      if isinstance(theoretical_dist, stats.norm.__class__):
          dist_name = "Normal"
      elif isinstance(theoretical_dist, stats.t.__class__):
          dist_name = "T"
      else:
          dist_name = "Theoretical"
  
      for a, (period_num, ic) in zip(ax, ic.iteritems()):
          sm.qqplot(
              ic.replace(np.nan, 0.0).values,
              theoretical_dist,
              fit=True,
              line="45",
              ax=a,
          )
          a.set(
              title="{} Period IC {} Dist. Q-Q".format(
                  period_num, dist_name
              ),
              ylabel="Observed Quantile",
              xlabel="{} Distribution Quantile".format(dist_name),
          )
  
      return ax
  
imports: import numpy
  import pandas
  from scipy import stats
  import statsmodels.api
  import seaborn
  import matplotlib.cm
  import matplotlib.pyplot
  from matplotlib.ticker import ScalarFormatter
  from functools import wraps
  from .None import utils
  from .None import performance
label_desc: Use image node_id 10 for calling a global function with example usage: plot_ic_qq(ic, theoretical_dist, ax) and returns: ax