code: def predict_one_season(
      m, quantile, name, n_steps=100, df_name="__df__"
  ):
      """
       Predicts the seasonal component given a number of time steps.
  
      Parameters
      ----------
           m : NeuralProphet
               Fitted NeuralProphet model
           quantile: float
               The quantile for which the season is predicted
           name: str
               Name of seasonality component
           n_steps: int
               number of prediction steps related to the season frequency
           df_name: str
                  Name of dataframe to refer to data params from original keys of train dataframes
  
          Returns
          -------
              t_i: np.array
                   time scale of predicted seasonal component
              predicted: OrderedDict
                   predicted seasonal component
  
      """
      config = m.config_seasonality.periods[name]
      t_i = np.arange(n_steps + 1) / float(n_steps)
      features = time_dataset.fourier_series_t(
          t=t_i * config.period,
          period=config.period,
          series_order=config.resolution,
      )
      features = torch.from_numpy(np.expand_dims(features, 1))
  
      if df_name == "__df__":
          meta_name_tensor = None
      else:
          meta = OrderedDict()
          meta["df_name"] = [df_name for _ in range(n_steps + 1)]
          meta_name_tensor = torch.tensor(
              [m.model.id_dict[i] for i in meta["df_name"]]
          )
  
      quantile_index = m.model.quantiles.index(quantile)
      predicted = m.model.seasonality.compute_fourier(
          features=features, name=name, meta=meta_name_tensor
      )[:, :, quantile_index]
      predicted = predicted.squeeze().detach().numpy()
      if m.config_seasonality.mode == "additive":
          data_params = m.config_normalization.get_data_params(df_name)
          scale = data_params["y"].scale
          predicted = predicted * scale
      return t_i, predicted
  
file_path: neural_prophet/neuralprophet/plot_utils.py
imports: import logging
  import warnings
  from collections import OrderedDict
  from typing import Optional
  import numpy
  import torch
  from neuralprophet import time_dataset, utils_torch
label_desc: Use image node_id 5 for calling a global function with example usage: predict_one_season(m, quantile, name, n_steps, df_name) and returns: t_i,  predicted