Only using the below imports, finish this question

from tmtoolkit.corpus import Corpus, lemmatize, to$\_$lowercase, remove$\_$chars, filter$\_$clean$\_$tokens

from tmtoolkit.corpus import corpus$\_$num$\_$tokens, corpus$\_$tokens$\_$flattened

from tmtoolkit.corpus import dtm

from tmtoolkit.corpus import vocabulary

from tmtoolkit.topicmod.model$\_$io import print$\_$ldamodel$\_$topic$\_$words

from tmtoolkit.topicmod.tm$\_$lda import compute$\_$models$\_$parallel

from string import punctuation

def build$\_$corpus$($texts$,$ lang$="$en$")$:

$"""$Corpus builder which returns a Corpus object processed on texts as language

specified by lang $($defaults to $"$en$")$:

Should perform all of the following pre$-$processing functions:

$-$ Lemmatize the tokens

$-$ Convert tokens to lowercase

$-$ Remove punctuation

$-$ Remove numbers

$-$ Remove tokens shorter than $2$ characters

$"""$

# Here, we just use the index of the text as the label for the corpus item

corpus $=$ Corpus$(\{$ i:r for i$,$ r in enumerate$($texts$)\},$ language$=$lang$)$

# TODO: Complete the implementation of this function and submit the

# $.$py download of this notebook as your assignment submission.

Use this for testing:

example$\_$docs $=[$ # Feel free to edit this corpus for further testing

# to be sure that your functions meet specifications.

"The $3$ cats sat on the mats!",

$"1$ fish $2$ fish Red fish Blue fish",

"She sells $ea$shells"

$]$

example$\_$corpus $=$ build$\_$corpus$($example$\_$docs$)$

corpus$\_$tokens$\_$flattened$($example$\_$corpus$)$