def get_most_similar_docs_docs2vec(letter_year, model, corpus, n, initial_year=1977):
    """Get the docs2vec most similar years

    Parameters
    ----------
    letter_year: int
        The target letter year
    model: docs2vec
        The trained Docs2vec model
    corpus: List
        TaggedDocument list
    n: int
        The number of letters to return
    initial_year: int
        The initial letter year

    Returns
    -------
    List
        List with the letter year sorted descending by similarity
    """
    doc_id = letter_year - initial_year
    inferred_vector = model.infer_vector(corpus[doc_id].words)
    sims = model.docvecs.most_similar([inferred_vector], topn=len(model.docvecs))
    sims = [index + initial_year for index, _ in sims]

    return sims[1:n + 1]