code: def get_prophet_seasonality(time_int, seasonal_period):
      """
      This returns the prophet seasonality if sent in the time interval.
      """
      prophet_seasonality = None
      if seasonal_period is not None:
          prophet_period = seasonal_period
      if time_int in ["MS", "M", "SM", "BM", "CBM", "SMS", "BMS"]:
          prophet_seasonality = "monthly"
          if seasonal_period is None:
              prophet_period = 30.5
          fourier_order = 12
          prior_scale = 0.1
      elif time_int in ["D", "B", "C"]:
          prophet_seasonality = "daily"
          if seasonal_period is None:
              prophet_period = 1
          fourier_order = 15
          prior_scale = 0.1
      elif time_int in ["W"]:
          prophet_seasonality = "weekly"
          if seasonal_period is None:
              prophet_period = 7
          fourier_order = 20
          prior_scale = 0.1
      elif time_int in ["Q", "BQ", "QS", "BQS"]:
          prophet_seasonality = "quarterly"
          if seasonal_period is None:
              prophet_period = 365.25 / 4
          fourier_order = 5
          prior_scale = 0.1
      elif time_int in ["A,Y", "BA,BY", "AS,YS", "BAS,YAS"]:
          prophet_seasonality = "yearly"
          if seasonal_period is None:
              prophet_period = 365.25
          fourier_order = 5
          prior_scale = 0.1
      elif time_int in ["BH", "H", "h"]:
          prophet_seasonality = "hourly"
          if seasonal_period is None:
              prophet_period = 24
          fourier_order = 5
          prior_scale = 0.1
      elif time_int in ["T,min"]:
          prophet_seasonality = "hourly"
          if seasonal_period is None:
              prophet_period = 24
          fourier_order = 12
          prior_scale = 0.1
      elif time_int in [
          "S",
          "L,milliseconds",
          "U,microseconds",
          "N,nanoseconds",
      ]:
          prophet_seasonality = "hourly"
          if seasonal_period is None:
              prophet_period = 24
          fourier_order = 12
          prior_scale = 0.1
      else:
          #### Monthly is the default ###
          prophet_seasonality = "monthly"
          if seasonal_period is None:
              prophet_period = 30.5
          fourier_order = 12
          prior_scale = 0.1
      return (
          prophet_seasonality,
          prophet_period,
          fourier_order,
          prior_scale,
      )
  
imports: from typing import Optional
  import logging
  import copy
  import time
  import numpy
  from sklearn.model_selection import TimeSeriesSplit
  import pandas
  from pandas.core.generic import NDFrame
  import pdb
  import dask
  import dask.dataframe
  import matplotlib.pyplot
  from prophet import Prophet
  from prophet.diagnostics import cross_validation
  from prophet.diagnostics import performance_metrics
  from prophet.plot import plot_cross_validation_metric
  from .build_base import BuildBase
  from ..utils import print_dynamic_rmse, quick_ts_plot, print_ts_model_stats
  from ..utils.logging import SuppressStdoutStderr
  from sklearn.metrics import mean_squared_error
  from prophet import Prophet
  import time
  import pdb
  import copy
  import matplotlib.pyplot
label_desc: Use image node_id 3 for calling a global function with example usage: get_prophet_seasonality(time_int, seasonal_period) and returns: prophet_seasonality,  prophet_period,  fourier_order,  prior_scale