Apply a function row by row using other dataframes' rows as list inputs in python

I'm trying to apply a function row-by-row which takes 5 inputs, 3 of which are lists. I want these lists to come from each row of 3 correspondings dataframes.

I've tried using 'apply' and 'lambda' as follows:

sol['tf_dd']=sol.apply(lambda tsol, rfsol, rbsol: 
                           taurho_difdif(xy=xy,
                                         l=l,
                                         t=tsol,
                                         rf=rfsol,
                                         rb=rbsol),
                           axis=1)

However I get the error <lambda>() missing 2 required positional arguments: 'rfsol' and 'rbsol'

The DataFrame sol and the DataFrames tsol, rfsol and rbsol all have the same length. For each row, I want the entire row from tsol, rfsol and rbsol to be input as three lists.

Here is much simplified example (first with single lists, which I then want to replicate row by row with dataframes):

The output with single lists is a single value (120). With dataframes as inputs I want an output dataframe of length 10 where all values are 120.

t=[1,2,3,4,5]
rf=[6,7,8,9,10]
rb=[11,12,13,14,15]

def simple_func(t, rf, rb):
    x=sum(t)
    y=sum(rf)
    z=sum(rb)

    return x+y+z

out=simple_func(t,rf,rb)

# dataframe rows as lists
tsol=pd.DataFrame((t,t,t,t,t,t,t,t,t,t))
rfsol=pd.DataFrame((rf,rf,rf,rf,rf,rf,rf,rf,rf,rf))
rbsol=pd.DataFrame((rb,rb,rb,rb,rb,rb,rb,rb,rb,rb))


out2 = pd.DataFrame(index=range(len(tsol)), columns=['output'])
out2['output'] = out2.apply(lambda tsol, rfsol, rbsol:
                            simple_func(t=tsol.tolist(),
                                        rf=rfsol.tolist(),
                                        rb=rbsol.tolist()),
                            axis=1)