This has to be written in python. Please help! Thanks!

Create a class Document Index to act as an abstract data type for the document index inverted index data structure. It should include the following member functions/support these operation on its data: A normalize(term) method that takes a str object term and returns a stemmed, lowercase version of that word suitable for a key in the inverted index. An update entry (normalized_term, doc.id): method that adds the normalized str object normalized_term to the index if it's not already in the index and records that the document with integral doc-id contains that term. A tokenize(document) method that takes a document as a str object and returns a list of unnormalized tokens contained in that document. Use a regex instead of split() for tokenization. An add_document (document, doc.id) method that takes a document as a str object with integral doc.id and adds a tokenized, normalized version of the document to the inverted index. Stopwords in the document are not indexed. Note that when the spec says a tokenized, normalized version of the document gets indexed, that doesn't imply this method implements that. It implies this method causes that to happen. Note the other methods above implement this functionality, so they will be called by add_document(). A build index(corpus) method that takes corpus as a list of str containing items that are the HTML of each document. Note that this corpus has no document ids, so use a document's index in the list as its ID here. An object of type Document Index should support the operator [term] for term lookup. In other words, if object ii was constructed via ii = Document Index() and a suitable index built with build index(), then ii['Kimmer'] would return the set of document IDs containing the search term 'Kimmer'. Hint: magic methods By default, if a term is not in the index, it should return the empty set. Create a class Document Index to act as an abstract data type for the document index inverted index data structure. It should include the following member functions/support these operation on its data: A normalize(term) method that takes a str object term and returns a stemmed, lowercase version of that word suitable for a key in the inverted index. An update entry (normalized_term, doc.id): method that adds the normalized str object normalized_term to the index if it's not already in the index and records that the document with integral doc-id contains that term. A tokenize(document) method that takes a document as a str object and returns a list of unnormalized tokens contained in that document. Use a regex instead of split() for tokenization. An add_document (document, doc.id) method that takes a document as a str object with integral doc.id and adds a tokenized, normalized version of the document to the inverted index. Stopwords in the document are not indexed. Note that when the spec says a tokenized, normalized version of the document gets indexed, that doesn't imply this method implements that. It implies this method causes that to happen. Note the other methods above implement this functionality, so they will be called by add_document(). A build index(corpus) method that takes corpus as a list of str containing items that are the HTML of each document. Note that this corpus has no document ids, so use a document's index in the list as its ID here. An object of type Document Index should support the operator [term] for term lookup. In other words, if object ii was constructed via ii = Document Index() and a suitable index built with build index(), then ii['Kimmer'] would return the set of document IDs containing the search term 'Kimmer'. Hint: magic methods By default, if a term is not in the index, it should return the empty set