<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 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 ------- str 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()