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()`