code: def _find_relevant_timestamp_attributes(series: TimeSeries) -> set:
      """Finds pd.Timestamp attributes relevant for seasonality.
  
      Analyzes the given TimeSeries instance for relevant pd.Timestamp attributes
      in terms of the autocorrelation of their length within the series with the
      goal of finding the periods of the seasonal trends present in the series.
  
      Parameters
      ----------
      series
          The TimeSeries instance to be analyzed.
  
      Returns
      -------
      set
          A set of pd.Timestamp attributes with high autocorrelation within `series`.
      """
      relevant_attributes = set()
  
      if type(series.freq) in {
          pd.tseries.offsets.MonthBegin,
          pd.tseries.offsets.MonthEnd,
      }:
          # check for yearly seasonality
          if _check_approximate_seasonality(series, 12, 1, 0):
              relevant_attributes.add("month")
      elif type(series.freq) == pd.tseries.offsets.Day:
          # check for yearly seasonality
          if _check_approximate_seasonality(series, 365, 5, 20):
              relevant_attributes.update({"month", "day"})
          # check for monthly seasonality
          elif _check_approximate_seasonality(series, 30, 2, 2):
              relevant_attributes.add("day")
          # check for weekly seasonality
          elif _check_approximate_seasonality(series, 7, 0, 0):
              relevant_attributes.add("weekday")
      elif type(series.freq) == pd.tseries.offsets.Hour:
          # check for yearly seasonality
          if _check_approximate_seasonality(series, 8760, 100, 100):
              relevant_attributes.update({"month", "day", "hour"})
          # check for monthly seasonality
          elif _check_approximate_seasonality(series, 730, 10, 30):
              relevant_attributes.update({"day", "hour"})
          # check for weekly seasonality
          elif _check_approximate_seasonality(series, 168, 3, 10):
              relevant_attributes.update({"weekday", "hour"})
          # check for daily seasonality
          elif _check_approximate_seasonality(series, 24, 1, 1):
              relevant_attributes.add("hour")
      elif type(series.freq) == pd.tseries.offsets.Minute:
          # check for daily seasonality
          if _check_approximate_seasonality(series, 1440, 20, 50):
              relevant_attributes.update({"hour", "minute"})
          # check for hourly seasonality
          elif _check_approximate_seasonality(series, 60, 4, 3):
              relevant_attributes.add("minute")
  
      return relevant_attributes
  
imports: from typing import Callable, Optional
  import numpy
  import pandas
  from statsmodels.tsa.stattools import acf
  from darts.logging import get_logger
  from darts.models.forecasting.forecasting_model import LocalForecastingModel
  from darts.timeseries import TimeSeries
  from darts.utils.missing_values import fill_missing_values
label_desc: Use image node_id 2 for calling a global function with example usage: _find_relevant_timestamp_attributes(series) and returns: relevant_attributes