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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def _convert_listlike(arg, unit='ns', box=True, errors='raise', name=None):
 """Convert a list of objects to a timedelta index object."""


    if isinstance(arg, (list, tuple)) or not hasattr(arg, 'dtype'):
        # This is needed only to ensure that in the case where we end up
        #  returning arg (errors == "ignore"), and where the input is a
        #  generator, we return a useful list-like instead of a
        #  used-up generator
        arg = np.array(list(arg), dtype=object)

    try:
        value = sequence_to_td64ns(arg, unit=unit,
                                   errors=errors, copy=False)[0]
    except ValueError:
        if errors == 'ignore':
            return arg
        else:
            # This else-block accounts for the cases when errors='raise'
            # and errors='coerce'. If errors == 'raise', these errors
            # should be raised. If errors == 'coerce', we shouldn't
            # expect any errors to be raised, since all parsing errors
            # cause coercion to pd.NaT. However, if an error / bug is
            # introduced that causes an Exception to be raised, we would
            # like to surface it.
            raise

    if box:
        from pandas import TimedeltaIndex
        value = TimedeltaIndex(value, unit='ns', name=name)
    return value