How is the TF-IDF values are calculated in scikit-learn by Python and how to seize the same result below ?

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Python2.7

Python3.3

How is the TF-IDF values are calculated in scikit-learn by python and how to seize the same result below ????

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Document 1 : ['includ', 'name', 'function', 'type', 'argument']

Document 2 : ['name', 'function', 'type', 'argument']

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##I run the following code to calculate tf-idf for the terms in both Doc 1 and Doc 2

tfidf = TfidfVectorizer(tokenizer=processData, stop_words='english')

tfs = tfidf.fit_transform(rawContentDict.values())

tfs_Values = tfs.toarray()

tfs_Term = tfidf.get_feature_names()

I get the following output of tf-idf values :

Document 1 : [includ = 0.630099, name = 0.448320, function = 0.448320 , type = 0.448320, argument = 0.448320]

Document 2 : [includ = 0 , name= 0.577350 , function = 0.577350 , type= 0.577350, argument= 0.577350]

Now I don't understand how these scores are computed. I tried but I got different results than the program output. How is the TF-IDF score calculated in scikit-learn and how to seize the same result above . ?? Your help is much appreciated

What I have tried:

i read this helpful contents [1] , [2] and implemnt the mentioned steps and still don't get the same results

[1] https://towardsdatascience.com/measure-text-weight-using-tf-idf-in-python-plain-code-and-scikit-learn-50cb1e4375ad

[2] https://stackoverflow.com/questions/36966019/how-aretf-idf-calculated-by-the-scikit-learn-tfidfvectorizer?noredirect=1&lq=1


Best close result I got by following [1] stpes is
[ includ = 0.57496]
and the one i want is [ includ = 0.630099 ]