class_docstr: Not sure if I can avoid the state machine here
docstr: Find the appropriate frequency string to describe the inferred
  frequency of self.i8values
  
  Returns
  -------
  str or None
docstr_tok: ["Find","the","appropriate","frequency","string","to","describe","the","inferred","frequency","of","self.i8values","Returns","--","--","--","-","str","or","None"]
code: def get_freq(self) -> str | None:
      """
      Find the appropriate frequency string to describe the inferred
      frequency of self.i8values
  
      Returns
      -------
      str or None
      """
      if not self.is_monotonic or not self.index._is_unique:
          return None
  
      delta = self.deltas[0]
      ppd = periods_per_day(self._creso)
      if delta and _is_multiple(delta, ppd):
          return self._infer_daily_rule()
  
      # Business hourly, maybe. 17: one day / 65: one weekend
      if self.hour_deltas in ([1, 17], [1, 65], [1, 17, 65]):
          return "bh"
  
      # Possibly intraday frequency.  Here we use the
      # original .asi8 values as the modified values
      # will not work around DST transitions.  See #8772
      if not self.is_unique_asi8:
          return None
  
      delta = self.deltas_asi8[0]
      pph = ppd // 24
      ppm = pph // 60
      pps = ppm // 60
      if _is_multiple(delta, pph):
          # Hours
          return _maybe_add_count("h", delta / pph)
      elif _is_multiple(delta, ppm):
          # Minutes
          return _maybe_add_count("min", delta / ppm)
      elif _is_multiple(delta, pps):
          # Seconds
          return _maybe_add_count("s", delta / pps)
      elif _is_multiple(delta, (pps // 1000)):
          # Milliseconds
          return _maybe_add_count("ms", delta / (pps // 1000))
      elif _is_multiple(delta, (pps // 1_000_000)):
          # Microseconds
          return _maybe_add_count("us", delta / (pps // 1_000_000))
      else:
          # Nanoseconds
          return _maybe_add_count("ns", delta)
  
imports: from __future__ import annotations
  from typing import TYPE_CHECKING
  import numpy
  from pandas._libs import lib
  from pandas._libs.algos import unique_deltas
  from pandas._libs.tslibs import Timestamp, get_unit_from_dtype, periods_per_day, tz_convert_from_utc
  from pandas._libs.tslibs.ccalendar import DAYS, MONTH_ALIASES, MONTH_NUMBERS, MONTHS, int_to_weekday
  from pandas._libs.tslibs.dtypes import OFFSET_TO_PERIOD_FREQSTR, freq_to_period_freqstr
  from pandas._libs.tslibs.fields import build_field_sarray, month_position_check
  from pandas._libs.tslibs.offsets import DateOffset, Day, to_offset
  from pandas._libs.tslibs.parsing import get_rule_month
  from pandas.util._decorators import cache_readonly
  from pandas.core.dtypes.common import is_numeric_dtype
  from pandas.core.dtypes.dtypes import DatetimeTZDtype, PeriodDtype
  from pandas.core.dtypes.generic import ABCIndex, ABCSeries
  from pandas.core.algorithms import unique
label_desc: Use image node_id 6 for calling the _FrequencyInferer obj's underlying member method code with example usage: obj.get_freq() and returns: None, self, str, None, _maybe_add_count, _maybe_add_count, _maybe_add_count, _maybe_add_count, _maybe_add_count, _maybe_add_count