code: def get_season_length(frequency):
      """Infers best season length from frequency parameter.
  
      We set a sensible default for seasonality based on the
      frequency parameter. For example: we assume monthly data
      has a season length of 12 (months in a year). If the inferred frequency
      isn't found, we default to 1 i.e. no seasonality.
      """
      season_dict = {  # https://pandas.pydata.org/docs/user_guide/timeseries.html#timeseries-offset-aliases
          "H": 24,
          "M": 12,
          "MS": 12,
          "Q": 4,
          "SM": 24,
          "BM": 12,
          "BMS": 12,
          "BQ": 4,
          "BH": 24,
      }
      new_freq = (
          frequency.split("-")[0] if "-" in frequency else frequency
      )  # shortens longer frequencies like Q-DEC
      return season_dict[new_freq] if new_freq in season_dict else 1
  
file_path: mindsdb/mindsdb/integrations/handlers/statsforecast_handler/statsforecast_handler.py
imports: import pandas
  import dill
  from mindsdb.integrations.libs.base import BaseMLEngine
  from mindsdb.integrations.utilities.time_series_utils import transform_to_nixtla_df, get_results_from_nixtla_df, infer_frequency, get_best_model_from_results_df, get_model_accuracy_dict, reconcile_forecasts, get_hierarchy_from_df
  from sklearn.metrics import r2_score
  from statsforecast import StatsForecast
  from statsforecast.models import AutoARIMA, AutoCES, AutoETS, AutoTheta
label_desc: Use image node_id 1 for calling a global function with example usage: get_season_length(frequency) and returns: unknown