Example code to understand pytorch padding

## test 
import torch
import torch.nn as nn

batch_size = 3
max_length = 3

batch_in = torch.LongTensor(batch_size, max_length).zero_()
batch_in[0] = torch.LongTensor([1, 2, 3])
batch_in[1] = torch.LongTensor([4, 5, 0])
batch_in[2] = torch.LongTensor([6, 0, 0])

seq_lengths = [3, 2, 1] 
# real seq lengths which means non-zero(padding) element in batch_in

print(batch_in)

rnn = torch.nn.GRU(10,16, batch_first=True)
emb = torch.nn.Embedding(20, 10)
t1 = emb(batch_in)
t2 = torch.nn.utils.rnn.pack_padded_sequence(t1, seq_lengths, batch_first=True)
t1.shape

y1, _ = rnn(t1)
y2, _ = rnn(t2)
y2, _ = torch.nn.utils.rnn.pad_packed_sequence(y2)

print(y1.shape)
print(y2.shape)

print(y1)
tensor([[[-0.2798, -0.3856, -0.0821,  0.1419, -0.0711,  0.3442, -0.2280,
          -0.3013,  0.4323, -0.0061, -0.0059,  0.0153,  0.0043, -0.0575,
          -0.0899,  0.0167],
         [-0.3635, -0.1256, -0.1606,  0.0027, -0.1943,  0.2244, -0.2939,
          -0.4380,  0.0758,  0.0657,  0.2524,  0.1803,  0.3322, -0.1517,
          -0.0686,  0.2098],
         [-0.5649,  0.1938,  0.1958, -0.1223,  0.2357,  0.2236, -0.1336,
          -0.4538, -0.0373,  0.0701,  0.2694,  0.1607,  0.0233, -0.4146,
          -0.2436, -0.1231]],

        [[-0.1592,  0.1595, -0.1047,  0.1702,  0.2267,  0.0230, -0.1799,
           0.0939,  0.0281,  0.3224,  0.0434, -0.0119, -0.1329, -0.3400,
          -0.1222, -0.1626],
         [-0.3771, -0.0135, -0.3905, -0.2723, -0.0649,  0.0405, -0.4896,
          -0.3519,  0.5476,  0.5207,  0.1581, -0.3331, -0.2819, -0.4226,
          -0.5203, -0.1131],
         [-0.4858,  0.2086, -0.2146, -0.2873,  0.1970, -0.0200, -0.3474,
          -0.3273, -0.1428,  0.3200,  0.3431, -0.1556,  0.0167, -0.4716,
          -0.4962,  0.0401]],

        [[-0.2618, -0.0852,  0.3207,  0.1903, -0.1152,  0.0338, -0.0622,
           0.2939,  0.2542,  0.2764,  0.1221,  0.2355, -0.2659,  0.3058,
           0.0794, -0.1431],
         [-0.4471,  0.2109,  0.1460, -0.1279,  0.2117, -0.0292, -0.0887,
           0.1132, -0.2051,  0.2217,  0.2989,  0.0810,  0.0741, -0.0756,
          -0.0479,  0.1462],
         [-0.5627,  0.3191,  0.0562, -0.2493,  0.3844, -0.0904, -0.1191,
          -0.0179, -0.4021,  0.1875,  0.3719, -0.0419,  0.1960, -0.2602,
          -0.1424,  0.2273]]], grad_fn=)

print(y2)
tensor([[[-0.2798, -0.3856, -0.0821,  0.1419, -0.0711,  0.3442, -0.2280,
          -0.3013,  0.4323, -0.0061, -0.0059,  0.0153,  0.0043, -0.0575,
          -0.0899,  0.0167],
         [-0.1592,  0.1595, -0.1047,  0.1702,  0.2267,  0.0230, -0.1799,
           0.0939,  0.0281,  0.3224,  0.0434, -0.0119, -0.1329, -0.3400,
          -0.1222, -0.1626],
         [-0.2618, -0.0852,  0.3207,  0.1903, -0.1152,  0.0338, -0.0622,
           0.2939,  0.2542,  0.2764,  0.1221,  0.2355, -0.2659,  0.3058,
           0.0794, -0.1431]],

        [[-0.3635, -0.1256, -0.1606,  0.0027, -0.1943,  0.2244, -0.2939,
          -0.4380,  0.0758,  0.0657,  0.2524,  0.1803,  0.3322, -0.1517,
          -0.0686,  0.2098],
         [-0.3771, -0.0135, -0.3905, -0.2723, -0.0649,  0.0405, -0.4896,
          -0.3519,  0.5476,  0.5207,  0.1581, -0.3331, -0.2819, -0.4226,
          -0.5203, -0.1131],
         [ 0.0000,  0.0000,  0.0000,  0.0000,  0.0000,  0.0000,  0.0000,
           0.0000,  0.0000,  0.0000,  0.0000,  0.0000,  0.0000,  0.0000,
           0.0000,  0.0000]],

        [[-0.5649,  0.1938,  0.1958, -0.1223,  0.2357,  0.2236, -0.1336,
          -0.4538, -0.0373,  0.0701,  0.2694,  0.1607,  0.0233, -0.4146,
          -0.2436, -0.1231],
         [ 0.0000,  0.0000,  0.0000,  0.0000,  0.0000,  0.0000,  0.0000,
           0.0000,  0.0000,  0.0000,  0.0000,  0.0000,  0.0000,  0.0000,
           0.0000,  0.0000],
         [ 0.0000,  0.0000,  0.0000,  0.0000,  0.0000,  0.0000,  0.0000,
           0.0000,  0.0000,  0.0000,  0.0000,  0.0000,  0.0000,  0.0000,
           0.0000,  0.0000]]], grad_fn=)