Implement out-of-box interoperability with TF Serving models. Given a model that works in TF Serving, make it easy to use directly in a Flink program, e.g.:

• implementations of "predict" and "classification" signature
• MapFunction implementations w/ appropriate types (tf.Example, Array[Byte])
• Example code

Some snippets to look at:

• Serving a TensorFlow Model
• mnist_saved_model.py
• API definitions