Please write the code in Python, follow the prompts.

markov_text(s, n = 2, length = 200, seed = "Emma Woodhouse")

Emma Woodhouse ne goo thimser. John mile sawas amintrought will on I kink you kno but every sh inat he fing as sat buty aft from the it. She cousency ined, yount; ate nambery quirld diall yethery, yould hat earatte

markov_text(s, n = 4, length = 200, seed = "Emma Woodhouse")

Emma Woodhouse!Emma, as love, Kitty, only this person no infering ever, while, and tried very were no do be very friendly and into aid, Man's me to loudness of Harriet's. Harriet belonger opinion an

markov_text(s, n = 10, length = 200, seed = "Emma Woodhouse")

Emma Woodhouse's party could be acceptable to them, that if she ever were disposed to think of nothing but good. It will be an excellent charade remains, fit for any acquainted with the child was given up to them.

Write a function that generates synthetic text according to an n-th order Markov model. It should have the following arguments: . s, the input string of real text. n , the order of the model. length , the size of the text to generate. Use a default value of 100. seed , the initial string that gets the Markov model started. I used "Emma Woodhouse (the full name of the protagonist of the novel) as my seed , but any subset of s of length n+1 or larger will work. Demonstrate the output of your function for a couple different choices of the order n. Expected Output Here are a few examples of the output of this function. Because of randomness, your results won't look exactly like this, but they should be qualitatively similar. markov_text(s, n = 2, length 200, seed "Emma Woodhouse) Emma Woodhouse ne goo thimser. John mile sawas amintrought will on I kink you kno but every sh inat he fing as sat buty aft from the it. She cousency ined, yount; ate nambery quirld diall yethery, yould hat earatte markov_text(s, n = 4, length 200, seed "Emma Woodhouse) Emma Woodhouse! dly and into aid, Emma, as love, Kitty, only this person no infering ever, while, and tried very were no do be very frien Man's me to loudness of Harriet's. Harriet belonger opinion an markov_text (s, n = 10, length = 200, seed "Emma Woodhouse) Emma Woodhouse's party could be acceptable to them, that if she ever were disposed to think of nothing but good. It will be an excell ent charade remains, fit for any acquainted with the child was given up to them. Notes and Hints Hint: A good function for performing the random choice is the choices () function in the random module. You can use it like this: import random options weights ["One", "Two, "Three"] [1, 2, 3] # "Two" is twice as likely as "One", "Three three times as likely. random. choices (options, weights) ['One'] # output The first and second arguments must be lists of equal length. Note also that the return value is a list -- if you want the value in the list, you need to get it out via indexing. Hint: The first thing your function should do is call count_ngrams above to generate the required dictionary. Then, handle the logic described above in the main loop. In [ ]: # write markov_text() here In [ ]: # try out your function for a few different values of n In [ ]: # try another value of n! In [ ]: # try third value