BUG: groupby().apply() raise numpy ValueError when Series has multi index about modin HOT 1 OPEN

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Reproducible Example

import modin.pandas as pd

data1=pd.read_excel('abc.xlsx', header=[0,1]) # multiple headers

def anyFuncB(x):
    do something
    return x

def anyFuncA(x)
    x.loc[data1[('col0','col1')].apply(anyFuncB)] #here cause the error, apply() results in a pd.Series

data = pd.read_excel('def.xlsx')
data.groupby(by='col0').apply(anyFuncA)

Issue Description

By just applying dataframe0.apply(anyFunc0), everything was good.

After applying dataframe0.groupby().apply(anyFunc0), if another dataframe1 has multi index and it runs dataframe1[('col0', 'col1')].apply(anyFunc1), File "/usr/local/python3.10/lib/python3.10/site-packages/modin/pandas/series.py", line 713, in apply if result.name == self.index[0]: raises ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all(), because here result.name is a tuple with 2 items and self.index[0] is a numpy.int64, the result of comparison is a list contents two boolean values, my temp fix is adding following code:

elif return_type == "Series":
    try:
        if result.name == self.index[0]:
            result.name = None
    except:
        if (result.name == self.index[0]).all():
            result.name = None

other solution could be to determine if result.name and self.index[0] is single value or not.

Expected Behavior

make the comparison correct

Error Logs

Installed Versions

solution modified to:

if  isinstance(_ := (result.name == self.index[0]), np.ndarray):
                if _.all():
                    result.name = None
            elif _:
                result.name = None

from modin.