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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def freeze_graph_tpu(model_path):
 """Custom freeze_graph implementation for Cloud TPU."""


    assert model_path
    assert FLAGS.tpu_name
    if FLAGS.tpu_name.startswith('grpc://'):
        tpu_grpc_url = FLAGS.tpu_name
    else:
        tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(
            FLAGS.tpu_name, zone=None, project=None)
        tpu_grpc_url = tpu_cluster_resolver.get_master()
    sess = tf.Session(tpu_grpc_url)

    output_names = []
    with sess.graph.as_default():
        # Replicate the inference function for each TPU core.
        replicated_features = []
        for i in range(FLAGS.num_tpu_cores):
            features = tf.placeholder(
                tf.float32, [None, go.N, go.N,
                             features_lib.NEW_FEATURES_PLANES],
                name='pos_tensor_%d' % i)
            replicated_features.append((features,))
        outputs = tf.contrib.tpu.replicate(
            tpu_model_inference_fn, replicated_features)

        # The replicate op assigns names like output_0_shard_0 to the output
        # names. Give them human readable names.
        for i, (policy_output, value_output, _) in enumerate(outputs):
            policy_name = 'policy_output_%d' % i
            value_name = 'value_output_%d' % i
            output_names.extend([policy_name, value_name])
            tf.identity(policy_output, policy_name)
            tf.identity(value_output, value_name)

        tf.train.Saver().restore(sess, model_path)

    # Freeze the graph.
    model_def = tf.graph_util.convert_variables_to_constants(
        sess, sess.graph.as_graph_def(), output_names)
    with tf.gfile.GFile(model_path + '.pb', 'wb') as f:
        f.write(model_def.SerializeToString())