Pandas v2.1.0 broke Market Calendars about pandas_market_calendars HOT 5 CLOSED

(venv311) MINGW64 ~/code/open-source/pandas_market_calendars_env
$ python --version
Python 3.11.3

(venv311) MINGW64 ~/code/open-source/pandas_market_calendars_env
$ pip list
Package                 Version
----------------------- -------
exchange-calendars      4.2.8
korean-lunar-calendar   0.3.1
numpy                   1.25.2
pandas                  2.1.0
pandas-market-calendars 4.2.1
pip                     22.3.1
pyluach                 2.2.0
python-dateutil         2.8.2
pytz                    2023.3
setuptools              65.5.0
six                     1.16.0
toolz                   0.12.0
tzdata                  2023.3

(venv311) MINGW64 ~/code/open-source/pandas_market_calendars_env
$ cat example.py
import pandas_market_calendars as mcal

# Create a calendar
nyse = mcal.get_calendar('NYSE')

# Show available calendars
print(mcal.get_calendar_names())

(venv311) MINGW64 ~/code/open-source/pandas_market_calendars_env
$ python example.py
['ASX', 'BMF', 'B3', 'CFE', 'CBOE_Futures', 'CBOE_Equity_Options', 'CBOE_Index_Options', 'CME_Equity', 'CBOT_Equity', 'CME_Agriculture', 'CBOT_Agriculture', 'COMEX_Agriculture', 'NYMEX_Agriculture', 'CME_Rate', 'CBOT_Rate', 'CME_InterestRate', 'CBOT_InterestRate', 'CME_Bond', 'CBOT_Bond', 'CMEGlobex_Livestock', 'CMEGlobex_Live_Cattle', 'CMEGlobex_Feeder_Cattle', 'CMEGlobex_Lean_Hog', 'CMEGlobex_Port_Cutout', 'CMEGlobex_FX', 'CME_FX', 'CME_Currency', 'CMEGlobex_EnergyAndMetals', 'CMEGlobex_Energy', 'CMEGlobex_CrudeAndRefined', 'CMEGlobex_NYHarbor', 'CMEGlobex_HO', 'HO', 'CMEGlobex_Crude', 'CMEGlobex_CL', 'CL', 'CMEGlobex_Gas', 'CMEGlobex_RB', 'RB', 'CMEGlobex_MicroCrude', 'CMEGlobex_MCL', 'MCL', 'CMEGlobex_NatGas', 'CMEGlobex_NG', 'NG', 'CMEGlobex_Dutch_NatGas', 'CMEGlobex_TTF', 'TTF', 'CMEGlobex_LastDay_NatGas', 'CMEGlobex_NN', 'NN', 'CMEGlobex_CarbonOffset', 'CMEGlobex_CGO', 'CGO', 'C-GEO', 'CMEGlobex_NGO', 'NGO', 'CMEGlobex_GEO', 'GEO', 'CMEGlobex_Metals', 'CMEGlobex_PreciousMetals', 'CMEGlobex_Gold', 'CMEGlobex_GC', 'GC', 'CMEGlobex_SilverCMEGlobex_SI', 'SI', 'CMEGlobex_Platinum', 'CMEGlobex_PL', 'PL', 'CMEGlobex_BaseMetals', 'CMEGlobex_Copper', 'CMEGlobex_HG', 'HG', 'CMEGlobex_Aluminum', 'CMEGlobex_ALI', 'ALI', 'CMEGlobex_QC', 'QC', 'CMEGlobex_FerrousMetals', 'CMEGlobex_HRC', 'HRC', 'CMEGlobex_BUS', 'BUS', 'CMEGlobex_TIO', 'TIO', 'CME Globex Equity', 'CME Globex Fixed Income', 'CME Globex Interest Rate Products', 'EUREX', 'HKEX', 'ICE', 'ICEUS', 'NYFE', 'NYSE', 'stock', 'NASDAQ', 'BATS', 'DJIA', 'DOW', 'IEX', 'Investors_Exchange', 'JPX', 'LSE', 'OSE', 'SIFMAUS', 'SIFMA_US', 'Capital_Markets_US', 'Financial_Markets_US', 'Bond_Markets_US', 'SIFMAUK', 'SIFMA_UK', 'Capital_Markets_UK', 'Financial_Markets_UK', 'Bond_Markets_UK', 'SIFMAJP', 'SIFMA_JP', 'Capital_Markets_JP', 'Financial_Markets_JP', 'Bond_Markets_JP', 'SIX', 'SSE', 'TSX', 'TSXV', 'BSE', 'NSE', 'TASE', 'AIXK', 'ASEX', 'BVMF', 'CMES', 'IEPA', 'XAMS', 'XASX', 'XBKK', 'XBOG', 'XBOM', 'XBRU', 'XBSE', 'XBUD', 'XBUE', 'XCBF', 'XCSE', 'XDUB', 'XFRA', 'XETR', 'XHEL', 'XHKG', 'XICE', 'XIDX', 'XIST', 'XJSE', 'XKAR', 'XKLS', 'XKRX', 'XLIM', 'XLIS', 'XLON', 'XMAD', 'XMEX', 'XMIL', 'XMOS', 'XNYS', 'XNZE', 'XOSL', 'XPAR', 'XPHS', 'XPRA', 'XSAU', 'XSES', 'XSGO', 'XSHG', 'XSTO', 'XSWX', 'XTAE', 'XTAI', 'XTKS', 'XTSE', 'XWAR', 'XWBO', 'us_futures', '24/7', '24/5']

(venv) PS C:\Users\User\code\> python --version
Python 3.11.4
(venv) PS C:\Users\User\code\> pip list
Package                 Version
----------------------- -------
exchange-calendars      4.2.8
korean-lunar-calendar   0.3.1
numpy                   1.25.2
pandas                  2.1.0
pandas-market-calendars 4.2.1
pip                     23.1.2
pyluach                 2.2.0
python-dateutil         2.8.2
pytz                    2023.3
setuptools              65.5.0
six                     1.16.0
toolz                   0.12.0
tzdata                  2023.3

import pandas as pd
from pandas.tseries.holiday import Holiday

def _observance(d):
    assert isinstance(d, pd.DatetimeIndex)
    return d[d.year < 2013]

h = Holiday(
    # When July 4th is a Thursday, the next day is a half day prior to 2013.
    # Since 2013 the early close is on Wednesday and Friday is a full day
    "Fridays after Independence Day prior to 2013",
    month=7,
    day=5,
    days_of_week=(4,),  # FRIDAY
    observance=_observance,
    start_date=pd.Timestamp("1996-01-01"),
)

result = h.dates(pd.Timestamp("1991-01-01"), pd.Timestamp("2023-01-01"))
print(result)