code: def plot_windows(cv, y, title="", ax=None):
      """Plot training and test windows.
  
      Plots the training and test windows for each split of a time series,
      subject to an sktime time series splitter.
  
      x-axis: time, ranging from start to end of `y`
      y-axis: window number, starting at 0
      plot elements: training split (orange) and test split (blue)
          dots indicate index in the training or test split
          will be plotted on top of each other if train/test split is not disjoint
  
      Parameters
      ----------
      y : pd.Series
          Time series to split
      cv : sktime splitter object, descendant of BaseSplitter
          Time series splitter, e.g., temporal cross-validation iterator
      title : str
          Plot title
      ax : matplotlib.axes.Axes, optional (default=None)
          Axes on which to plot. If None, axes will be created and returned.
  
      Returns
      -------
      fig : matplotlib.figure.Figure, returned only if ax is None
          matplotlib figure object
      ax : matplotlib.axes.Axes
          matplotlib axes object with the figure
      """
      _check_soft_dependencies("matplotlib", "seaborn")
      import matplotlib.pyplot as plt
      import seaborn as sns
      from matplotlib.ticker import MaxNLocator
  
      simplefilter("ignore", category=UserWarning)
  
      _ax_kwarg_is_none = True if ax is None else False
  
      # create figure if no ax provided for plotting
      if _ax_kwarg_is_none:
          fig, ax = plt.subplots(figsize=plt.figaspect(0.3))
  
      train_windows, test_windows = _get_windows(cv, y)
  
      def get_y(length, split):
          # Create a constant vector based on the split for y-axis."""
          return np.ones(length) * split
  
      n_splits = len(train_windows)
      n_timepoints = len(y)
      len_test = len(test_windows[0])
  
      train_color, test_color = sns.color_palette("colorblind")[:2]
  
      for i in range(n_splits):
          train = train_windows[i]
          test = test_windows[i]
  
          ax.plot(
              np.arange(n_timepoints),
              get_y(n_timepoints, i),
              marker="o",
              c="lightgray",
          )
          ax.plot(
              train,
              get_y(len(train), i),
              marker="o",
              c=train_color,
              label="Window",
          )
          ax.plot(
              test,
              get_y(len_test, i),
              marker="o",
              c=test_color,
              label="Forecasting horizon",
          )
      ax.invert_yaxis()
      ax.yaxis.set_major_locator(MaxNLocator(integer=True))
      xtickslocs = [
          tick
          for tick in ax.get_xticks()
          if tick in np.arange(n_timepoints)
      ]
      ax.set(
          title=title,
          ylabel="Window number",
          xlabel="Time",
          xticks=xtickslocs,
          xticklabels=y.iloc[xtickslocs].index,
      )
      # remove duplicate labels/handles
      handles, labels = (
          (leg[:2]) for leg in ax.get_legend_handles_labels()
      )
      ax.legend(handles, labels)
  
      if _ax_kwarg_is_none:
          return fig, ax
      else:
          return ax
  
file_path: sktime/sktime/utils/plotting.py
imports: import math
  from warnings import simplefilter, warn
  import numpy
  import pandas
  from sktime.datatypes import convert_to
  from sktime.utils.validation._dependencies import _check_soft_dependencies
  from sktime.utils.validation.forecasting import check_interval_df, check_y
  from sktime.utils.validation.series import check_consistent_index_type
label_desc: Use image node_id 7 for calling a global function with example usage: plot_windows(cv, y, title, ax) and returns: unknown, fig,  ax, ax