class_docstr: Not sure if I can avoid the state machine here
class_docstr_tok: ["Not","sure","if","I","can","avoid","the","state","machine","here"]
code: def __init__(self, index, warn=True):
      self.index = index
      self.values = index.asi8
  
      # This moves the values, which are implicitly in UTC, to the
      # the timezone so they are in local time
      if hasattr(index, "tz"):
          if index.tz is not None:
              self.values = tz_convert(self.values, UTC, index.tz)
  
      self.warn = warn
  
      if len(index) < 3:
          raise ValueError("Need at least 3 dates to infer frequency")
  
      self.is_monotonic = (
          self.index._is_monotonic_increasing
          or self.index._is_monotonic_decreasing
      )
  
imports: from datetime import timedelta
  import re
  import numpy
  from pytz import AmbiguousTimeError
  from pandas._libs.algos import unique_deltas
  from pandas._libs.tslibs import Timedelta, Timestamp
  from pandas._libs.tslibs.ccalendar import MONTH_ALIASES, int_to_weekday
  from pandas._libs.tslibs.conversion import tz_convert
  from pandas._libs.tslibs.fields import build_field_sarray
  import pandas._libs.tslibs.frequencies
  from pandas._libs.tslibs.offsets import _offset_to_period_map
  import pandas._libs.tslibs.resolution
  from pandas._libs.tslibs.resolution import Resolution
  from pandas._libs.tslibs.timezones import UTC
  import pandas.compat
  from pandas.compat import zip
  from pandas.util._decorators import cache_readonly
  from pandas.core.dtypes.common import is_datetime64_dtype, is_period_arraylike, is_timedelta64_dtype
  from pandas.core.dtypes.generic import ABCSeries
  from pandas.core.algorithms import unique
  from pandas.tseries.offsets import DateOffset, Day, Hour, Micro, Milli, Minute, Nano, Second, prefix_mapping
label_desc: Use image node_id 1 to create a new _FrequencyInferer object from inherited base classes: object with example: obj = _FrequencyInferer(index, warn)