IndexError: индекс 20 выходит за пределы оси 1 с размером 20

Question

Я все время получаю эту ошибку. Но проблема в том, что если я запускаю с размером final_target_1 350, он работает нормально. Все, что больше этого, дает мне вышеупомянутую ошибку. Это весь код, который я использую. Это как-то связано с параметром maxlen, который я передаю в последних 6 строках кода, но я не могу понять, в чем проблема.

# load the data
context = np.load('/content/context_indexes.npy',allow_pickle=True)
final_target = np.load('/content/target_indexes.npy',allow_pickle=True)
    with open('/content/dictionary.pkl', 'rb') as f:
    word_to_index = pickle.load(f)


# the indexes of the words start with 0. But when the sequences are padded 
later on, they too will be zeros.
# so, shift all the index values one position to the right, so that 0 is 
spared, and used only to pad the sequences
for i,j in word_to_index.items():
    word_to_index[i] = j+1

# reverse dictionary
index_to_word = {}
for k,v in word_to_index.items():
    index_to_word[v] = k

final_target_1 = final_target
context_1 = context
#temp soln: reducing the size of data to be processed-
#this is where I reduce the dataset size from 1000 to 350 because it will 
not run with anything larger than that
#final_target_1=final_target[:350]
#context_1 = context[:350]

maxLen = 30
# shift the indexes of the context and target arrays too
for i in final_target_1:
    for pos,j in enumerate(i): i[pos] = j + 1
for i in context_1:
    for pos,j in enumerate(i): i[pos] = j + 1


# read in the 50 dimensional GloVe embeddings
def read_glove_vecs(file):
    with open(file, 'r',encoding='utf-8') as f:
        words = set()
        word_to_vec_map = {}

        for line in f:
            line = line.strip().split()
            word = line[0]
            words.add(word)
            word_to_vec_map[word] = np.array(line[1:], dtype=np.float64)

    return words, word_to_vec_map

words, word_to_vec_map = read_glove_vecs('/content/drive/My 
Drive/glove.twitter.27B.50d.txt')

# since the indexes start from 1 and not 0, we add 1 to the no. of total 
words to get the vocabulary size (while initializing 
# and populating arrays later on, this will be required)
vocab_size = len(word_to_index) + 1

# initialize the embedding matrix that will be used (50 is the GloVe 
vector dimension)
embedding_matrix = np.zeros((vocab_size, 50))
for word,index in word_to_index.items():
    try:
        embedding_matrix[index, :] = word_to_vec_map[word.lower()]
    except: continue

outs = np.zeros((context_1.shape[0], maxLen, vocab_size))
    for pos,i in enumerate(final_target_1):
    for pos1,j in enumerate(i):
        if pos1 > 0:
            outs[pos, pos1 - 1, j] = 1
    if pos%1000 == 0: print ('{} entries completed'.format(pos))