I am having trouble understanding the graph argument in the tf.Session(). I tried looking up at the TensorFlow website :link but couldn't understand much.

I am trying to find out the different between tf.Session() and tf.Session(graph=some_graph_inserted_here).

Question Context

Code A (Not Working):

def predict():
    with tf.name_scope("predict"):
        with tf.Session() as sess:
            saver = tf.train.import_meta_graph("saved_models/testing.meta")
            saver.restore(sess, "saved_models/testing")
            loaded_graph = tf.get_default_graph()
            output_ = loaded_graph.get_tensor_by_name('loss/network/output_layer/BiasAdd:0')
            _x = loaded_graph.get_tensor_by_name('x:0')
            print sess.run(output_, feed_dict={_x: np.array([12003]).reshape([-1, 1])})

This code gives the following error: ValueError: cannot add op with name hidden_layer1/kernel/Adam as that name is already used when trying to load the graph at saver = tf.train.import_meta_graph("saved_models/testing.meta")

Code B (Working):

def predict():
    with tf.name_scope("predict"):
        loaded_graph = tf.Graph()
        with tf.Session(graph=loaded_graph) as sess:
            saver = tf.train.import_meta_graph("saved_models/testing.meta")
            saver.restore(sess, "saved_models/testing")
            output_ = loaded_graph.get_tensor_by_name('loss/network/output_layer/BiasAdd:0')
            _x = loaded_graph.get_tensor_by_name('x:0')
            print sess.run(output_, feed_dict={_x: np.array([12003]).reshape([-1, 1])})

The codes does not work if I replace loaded_graph = tf.Graph() with loaded_graph = tf.get_default_graph(). Why?

Full Code if it helps: (https://gist.github.com/duemaster/f8cf05c0923ebabae476b83e895619ab)