code: def infer_freq(index, warn=True):
      """
      Infer the most likely frequency given the input index. If the frequency is
      uncertain, a warning will be printed.
  
      Parameters
      ----------
      index : DatetimeIndex or TimedeltaIndex
        if passed a Series will use the values of the series (NOT THE INDEX)
      warn : boolean, default True
  
      Returns
      -------
      freq : string or None
          None if no discernible frequency
          TypeError if the index is not datetime-like
          ValueError if there are less than three values.
      """
      import pandas as pd
  
      if isinstance(index, ABCSeries):
          values = index._values
          if not (
              is_datetime64_dtype(values)
              or is_timedelta64_dtype(values)
              or values.dtype == object
          ):
              raise TypeError(
                  "cannot infer freq from a non-convertible dtype "
                  "on a Series of {dtype}".format(dtype=index.dtype)
              )
          index = values
  
      if is_period_arraylike(index):
          raise TypeError(
              "PeriodIndex given. Check the `freq` attribute "
              "instead of using infer_freq."
          )
      elif is_timedelta64_dtype(index):
          # Allow TimedeltaIndex and TimedeltaArray
          inferer = _TimedeltaFrequencyInferer(index, warn=warn)
          return inferer.get_freq()
  
      if isinstance(index, pd.Index) and not isinstance(
          index, pd.DatetimeIndex
      ):
          if isinstance(index, (pd.Int64Index, pd.Float64Index)):
              raise TypeError(
                  "cannot infer freq from a non-convertible index "
                  "type {type}".format(type=type(index))
              )
          index = index.values
  
      if not isinstance(index, pd.DatetimeIndex):
          try:
              index = pd.DatetimeIndex(index)
          except AmbiguousTimeError:
              index = pd.DatetimeIndex(index.asi8)
  
      inferer = _FrequencyInferer(index, warn=warn)
      return inferer.get_freq()
  
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 4 for calling a global function with example usage: infer_freq(index, warn) and returns: inferer, inferer