nn.embedding backward grad

gistfile1.txt

import torch
import torch.nn as nn

# an Embedding module containing 10 tensors of size 3
embedding = nn.Embedding(6, 3)
# a batch of 2 samples of 3 elements each
input = torch.LongTensor([[1, 2, 3],[0,2,3]])
loss=torch.sum(embedding(input))
print("embedding" )
print(embedding.weight )
print("input" )
print(input)

print("forward pass" )
print(embedding(input))

print("loss")
print(loss)

loss.backward()

print("embedding backward grad ")
print(embedding.weight.grad )