about skip-gram code

I don't quite understand that why '**batch_inputs**', '**batch_labels**' should be updated during each loop in Word2Vec-Skipgram-Tensor(Softmax).py .

Also ,what does '**trained_embeddings = W.eval()**' mean?

Could you explain it for me?I am a bit confused.

`# code

for epoch in range(5000):
batch_inputs, batch_labels = random_batch(skip_grams, batch_size)
_, loss = sess.run([optimizer, cost], feed_dict={inputs: batch_inputs, labels: batch_labels})

if (epoch + 1)%1000 == 0:
print('Epoch:', '%04d' % (epoch + 1), 'cost =', '{:.6f}'.format(loss))

trained_embeddings = W.eval()`

先阅读 Word2Vec-Skipgram-Tensor(Softmax).py，并跟踪其中展示的训练循环，尤其是 random_batch、batch_inputs 和 batch_labels。然后检查 trained_embeddings = W.eval() 这一行；当这两个操作及其在循环中的用途都得到清晰解释时，该 issue 就得到解决。

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