code: def get_lags_for_frequency(
      freq_str: str,
      lag_ub: int = 1200,
      num_lags: Optional[int] = None,
      num_default_lags: int = 7,
  ) -> List[int]:
      """
      Generates a list of lags that that are appropriate for the given frequency
      string.
  
      By default all frequencies have the following lags: [1, 2, 3, 4, 5, 6, 7].
      Remaining lags correspond to the same `season` (+/- `delta`) in previous
      `k` cycles. Here `delta` and `k` are chosen according to the existing code.
  
      Parameters
      ----------
  
      freq_str
          Frequency string of the form [multiple][granularity] such as "12H",
          "5min", "1D" etc.
  
      lag_ub
          The maximum value for a lag.
  
      num_lags
          Maximum number of lags; by default all generated lags are returned.
  
      num_default_lags
          The number of default lags; by default it is 7.
      """
  
      # Lags are target values at the same `season` (+/- delta) but in the
      # previous cycle.
      def _make_lags_for_second(multiple, num_cycles=3):
          # We use previous ``num_cycles`` hours to generate lags
          return [
              _make_lags(k * 60 // multiple, 2)
              for k in range(1, num_cycles + 1)
          ]
  
      def _make_lags_for_minute(multiple, num_cycles=3):
          # We use previous ``num_cycles`` hours to generate lags
          return [
              _make_lags(k * 60 // multiple, 2)
              for k in range(1, num_cycles + 1)
          ]
  
      def _make_lags_for_hour(multiple, num_cycles=7):
          # We use previous ``num_cycles`` days to generate lags
          return [
              _make_lags(k * 24 // multiple, 1)
              for k in range(1, num_cycles + 1)
          ]
  
      def _make_lags_for_day(
          multiple, num_cycles=4, days_in_week=7, days_in_month=30
      ):
          # We use previous ``num_cycles`` weeks to generate lags
          # We use the last month (in addition to 4 weeks) to generate lag.
          return [
              _make_lags(k * days_in_week // multiple, 1)
              for k in range(1, num_cycles + 1)
          ] + [_make_lags(days_in_month // multiple, 1)]
  
      def _make_lags_for_week(multiple, num_cycles=3):
          # We use previous ``num_cycles`` years to generate lags
          # Additionally, we use previous 4, 8, 12 weeks
          return [
              _make_lags(k * 52 // multiple, 1)
              for k in range(1, num_cycles + 1)
          ] + [[4 // multiple, 8 // multiple, 12 // multiple]]
  
      def _make_lags_for_month(multiple, num_cycles=3):
          # We use previous ``num_cycles`` years to generate lags
          return [
              _make_lags(k * 12 // multiple, 1)
              for k in range(1, num_cycles + 1)
          ]
  
      # multiple, granularity = get_granularity(freq_str)
      offset = to_offset(freq_str)
      # normalize offset name, so that both `W` and `W-SUN` refer to `W`
      offset_name = norm_freq_str(offset.name)
  
      if offset_name == "A":
          lags = []
      elif offset_name == "Q":
          assert (
              offset.n == 1
          ), "Only multiple 1 is supported for quarterly. Use x month instead."
          lags = _make_lags_for_month(offset.n * 3.0)
      elif offset_name == "M":
          lags = _make_lags_for_month(offset.n)
      elif offset_name == "W":
          lags = _make_lags_for_week(offset.n)
      elif offset_name == "D":
          lags = _make_lags_for_day(offset.n) + _make_lags_for_week(
              offset.n / 7.0
          )
      elif offset_name == "B":
          lags = _make_lags_for_day(
              offset.n, days_in_week=5, days_in_month=22
          ) + _make_lags_for_week(offset.n / 5.0)
      elif offset_name == "H":
          lags = (
              _make_lags_for_hour(offset.n)
              + _make_lags_for_day(offset.n / 24)
              + _make_lags_for_week(offset.n / (24 * 7))
          )
      # minutes
      elif offset_name == "T":
          lags = (
              _make_lags_for_minute(offset.n)
              + _make_lags_for_hour(offset.n / 60)
              + _make_lags_for_day(offset.n / (60 * 24))
              + _make_lags_for_week(offset.n / (60 * 24 * 7))
          )
      # second
      elif offset_name == "S":
          lags = (
              _make_lags_for_second(offset.n)
              + _make_lags_for_minute(offset.n / 60)
              + _make_lags_for_hour(offset.n / (60 * 60))
          )
      else:
          raise Exception("invalid frequency")
  
      # flatten lags list and filter
      lags = [
          int(lag)
          for sub_list in lags
          for lag in sub_list
          if 7 < lag <= lag_ub
      ]
      lags = list(range(1, num_default_lags + 1)) + sorted(
          list(set(lags))
      )
  
      return lags[:num_lags]
  
imports: from typing import List, Optional
  import numpy
  from pandas.tseries.frequencies import to_offset
  from gluonts.time_feature import norm_freq_str
label_desc: Use image node_id 2 for calling a global function with example usage: get_lags_for_frequency(freq_str, lag_ub, num_lags, num_default_lags) and returns: lags, unknown, unknown, unknown, unknown, unknown, unknown