def get_document_vecs(all_docs, en_embeddings):
    '''
    Input:
        - all_docs: list of strings - all tweets in our dataset.
        - en_embeddings: dictionary with words as the keys and their embeddings as the values.
    Output:
        - document_vec_matrix: matrix of tweet embeddings.
        - ind2Doc_dict: dictionary with indices of tweets in vecs as keys and their embeddings as the values.
    '''

    # the dictionary's key is an index (integer) that identifies a specific tweet
    # the value is the document embedding for that document
    ind2Doc_dict = {}

    # this is list that will store the document vectors
    document_vec_l = []

    for i, doc in enumerate(all_docs):

        ### START CODE HERE (REPLACE INSTANCES OF 'None' with your code) ###
        # get the document embedding of the tweet
        doc_embedding = get_document_embedding(doc, en_embeddings)

        # save the document embedding into the ind2Tweet dictionary at index i
        ind2Doc_dict[i] = doc_embedding

        # append the document embedding to the list of document vectors
        document_vec_l.append(doc_embedding)

        ### END CODE HERE ###

    # convert the list of document vectors into a 2D array (each row is a document vector)
    document_vec_matrix = np.vstack(document_vec_l)

    return document_vec_matrix, ind2Doc_dict