code: def plot_custom_season(
      m, comp_name, quantile, ax=None, figsize=(10, 6), df_name="__df__"
  ):
      """Plot any seasonal component of the forecast.
  
      Parameters
      ----------
          m : NeuralProphet
              Fitted model
          comp_name : str
              Name of seasonality component
          quantile : float
              The quantile for which the custom season is plotted
          ax : matplotlib axis
              Matplotlib Axes to plot on
          focus : int
              Show weights for this forecast, if provided
          figsize : tuple
              Width, height in inches, ignored if ax is not None.
  
              Note
              ----
              Default value is set to ``figsize = (10, 6)``
          df_name : str
              Name of dataframe to refer to data params from original keys of train dataframes
  
              Note
              ----
              Only used for local normalization in global modeling
  
      Returns
      -------
          matplotlib.artist.Artist
              List of Artist objects containing seasonal forecast component
  
      """
      t_i, predicted = predict_one_season(
          m,
          name=comp_name,
          n_steps=300,
          quantile=quantile,
          df_name=df_name,
      )
      artists = []
      if not ax:
          fig = plt.figure(facecolor="w", figsize=figsize)
          ax = fig.add_subplot(111)
      artists += ax.plot(t_i, predicted, ls="-", c="#0072B2")
      ax.grid(True, which="major", c="gray", ls="-", lw=1, alpha=0.2)
      ax.set_xlabel(f"One period: {comp_name}")
      ax.set_ylabel(f"Seasonality: {comp_name}")
      return artists
  
file_path: neural_prophet/neuralprophet/plot_model_parameters_matplotlib.py
imports: import datetime
  import logging
  import numpy
  import pandas
  from neuralprophet.plot_utils import predict_one_season, predict_season_from_dates, set_y_as_percent
label_desc: Use image node_id 6 for calling a global function with example usage: plot_custom_season(m, comp_name, quantile, ax, figsize, df_name) and returns: artists