# All Import Statements Defined Here

 # Note: Do not add to this list.

 # All the dependencies you need, can be installed by running .

 # ----------------

 import sys

 assert sys.version_info[0]==3

 assert sys.version_info[1] >= 5

 from gensim.models import KeyedVectors

 from gensim.test.utils import datapath

 import pprint

 import matplotlib.pyplot as plt

 plt.rcParams['figure.figsize'] = [10, 5]

 import nltk

 nltk.download('reuters')

 from nltk.corpus import reuters

 import numpy as np

 import random

 import scipy as sp

 from sklearn.decomposition import TruncatedSVD

 from sklearn.decomposition import PCA

 START_TOKEN = '<START>'

 END_TOKEN = '<END>'

 np.random.seed(0)

 random.seed(0)

 # ----------------

 def distinct_words(corpus):

     """ Determine a list of distinct words for the corpus.

         Params:

             corpus (list of list of strings): corpus of documents

         Return:

             corpus_words (list of strings): list of distinct words across the corpus, sorted (using python 'sorted' function)

             num_corpus_words (integer): number of distinct words across the corpus

     """

     corpus_words = []

     num_corpus_words = -1

     # ------------------

     # Write your implementation here.

     raw_corpus_words = [word for corpu in corpus for word in corpu]

     corpus_words = list(set(raw_corpus_words))

     corpus_words = sorted(corpus_words)

     num_corpus_words = len(corpus_words)

     # ------------------

     return corpus_words, num_corpus_words