code: def plot_yearly(
      m,
      ax=None,
      uncertainty=True,
      yearly_start=0,
      figsize=(10, 6),
      name="yearly",
  ):
      """Plot the yearly component of the forecast.
  
      Parameters
      ----------
      m: Prophet model.
      ax: Optional matplotlib Axes to plot on. One will be created if
          this is not provided.
      uncertainty: Optional boolean to plot uncertainty intervals, which will
          only be done if m.uncertainty_samples > 0.
      yearly_start: Optional int specifying the start day of the yearly
          seasonality plot. 0 (default) starts the year on Jan 1. 1 shifts
          by 1 day to Jan 2, and so on.
      figsize: Optional tuple width, height in inches.
      name: Name of seasonality component if previously changed from default 'yearly'.
  
      Returns
      -------
      a list of matplotlib artists
      """
      artists = []
      if not ax:
          fig = plt.figure(facecolor="w", figsize=figsize)
          ax = fig.add_subplot(111)
      # Compute yearly seasonality for a Jan 1 - Dec 31 sequence of dates.
      days = pd.date_range(
          start="2017-01-01", periods=365
      ) + pd.Timedelta(days=yearly_start)
      df_y = seasonality_plot_df(m, days)
      seas = m.predict_seasonal_components(df_y)
      artists += ax.plot(
          df_y["ds"].dt.to_pydatetime(), seas[name], ls="-", c="#0072B2"
      )
      if uncertainty and m.uncertainty_samples:
          artists += [
              ax.fill_between(
                  df_y["ds"].dt.to_pydatetime(),
                  seas[name + "_lower"],
                  seas[name + "_upper"],
                  color="#0072B2",
                  alpha=0.2,
              )
          ]
      ax.grid(True, which="major", c="gray", ls="-", lw=1, alpha=0.2)
      months = MonthLocator(range(1, 13), bymonthday=1, interval=2)
      ax.xaxis.set_major_formatter(
          FuncFormatter(
              lambda x, pos=None: "{dt:%B} {dt.day}".format(
                  dt=num2date(x)
              )
          )
      )
      ax.xaxis.set_major_locator(months)
      ax.set_xlabel("Day of year")
      ax.set_ylabel(name)
      if m.seasonalities[name]["mode"] == "multiplicative":
          ax = set_y_as_percent(ax)
      return artists
  
imports: from __future__ import absolute_import, division, print_function
  import logging
  import numpy
  import pandas
  from prophet.diagnostics import performance_metrics
label_desc: Use image node_id 6 for calling a global function with example usage: plot_yearly(m, ax, uncertainty, yearly_start, figsize, name) and returns: artists