Python to compute MSE for every row

Question

I am trying to compute the MSE for every row in my dataframe,

Below is my code-

import pandas as pd
s={'AValues':[1,1,2,2],'month':[2016,2017,2018,2019],'fvalues':[55,66,77,88],'Fruits':['Apple','Mango','Orange','Banana']}
p=pd.DataFrame(data=s)
mse_df=pd.pivot_table(p,index='Fruits',columns='month',values=['AValues','fvalues'])
mse_df=mse_df.fillna(0)

This is how the dataframe output of above code looks like-

I wanted to calculate here mse for Apples, Banana, Mango and Orange i.e row by row. (AValues- actual values , fvalues- forecasted values)

I am trying the below code-

from sklearn.metrics import mean_squared_error
mean_squared_error(mse_df['AValues'],mse_df['fvalues'])

But, it's giving me the total mse and not individual or row by row.

Can you please help me on how the mse can be found out?