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The `sparse` version is more efficient anyway.

I'm returning the labels shape [1] instead of []
because tf.accuracy fails otherwise.

Add multi-GPU option to MNIST

The `sparse` version is more efficient anyway.

I'm returning the labels shape [1] instead of []
because tf.accuracy fails otherwise.

This reverts commit e8726907.

Add multi-GPU flag to MNIST and allow for setting of replicated optimizer and model_fn

- Use the object-oriented tf.layers API instead of the functional one.
  The object-oriented API is particularly useful when using the model
  with eager execution.
- Update unittest to train, evaluate, and predict using the model.
- Add a micro-benchmark for measuring step-time.
  The parameters (batch_size, num_steps etc.) have NOT been tuned,
  the purpose with this code is mostly to illustrate how model
  benchmarks may be written.

These changes are made as a step towards consolidating model definitions
for different TensorFlow features (like eager execution and support for
TPUs in
https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/eager/python/examples/mnist
and
https://github.com/tensorflow/tpu-demos/tree/master/cloud_tpu/models/mnist