[Tensorflow] Tensorflow API r1.8.0

# Release 1.8.0

## Major Features And Improvements
* Can now pass `tf.contrib.distribute.MirroredStrategy()` to `tf.estimator.RunConfig()` to run an Estimator model on multiple GPUs on one machine.
* Add `tf.contrib.data.prefetch_to_device()`, which supports prefetching to GPU memory.
* Added Gradient Boosted Trees as pre-made Estimators: BoostedTreesClassifier, BoostedTreesRegressor.
* Add 3rd generation pipeline config for Cloud TPUs which improves performance and usability.
* `tf.contrib.bayesflow` is moving out to it's own repo.
* Added `tf.contrib.{proto,rpc}` to allow generic proto parsing and RPC communication.

## Bug Fixes and Other Changes
* `tf.data`:
  * Add `tf.contrib.data.prefetch_to_device`, which enables prefetching dataset elements to GPU memory.
  * Add `tf.contrib.data.AUTOTUNE`, which allows the tf.data runtime to automatically tune the prefetch buffer sizes based on your system and environment.
  * Add `tf.contrib.data.make_csv_dataset` for building datasets of CSV files.
* Eager Execution:
  * With eager execution Datasets can now be used as standard python iterators (`for batch in dataset:`). Both `Dataset.__iter__()` and `Dataset.make_one_shot_iterator()` can now be used to create iterators when eager execution is enabled.
  * Automatic device placement has been enabled (i.e., use a GPU if available automatically, without requiring an explicit `with tf.device(“/gpu:0”)`) (Fixes #14133)
  * `tf.GradientTape` has moved out of contrib.
* `tf.keras`:
  * Added the fashion mnist dataset.
  * New data preprocessing functions: `image/random_brightness`, `sequence/TimeseriesGenerator`, and `text/hashing_trick`.
* Accelerated Linear Algebra (XLA):
  * Select and scatter in reference util and evaluator now use lexicographical order to break ties.
* TensorFlow Debugger (tfdbg) CLI:
  * During tensor-filter operations, allow exclusion of nodes by regular expressions.
  * Fix spurious background colors in some text terminals.
* `tf.contrib`:
  * Add meta-distribution BatchReshape which reshapes batch dimensions.
  * `tf.contrib.layers.recompute_grad` works for explicit gradient checkpointing on TPU.
  * Add `tf.contrib.framework.argsort`.
  * Allow `DNNBoostedTreeCombinedEstimator` to work with core versions of feature columns and losses.
  * Add non-linear image warping ops: `tf.contrib.image.sparse_image_warp`, `tf.contrib.image.dense_image_warp`, and `tf.contrib.image.interpolate_spline`.
  * Fix bug in `tf.contrib.opt.MultitaskOptimizerWrapper` where types of tensors were mismatched.
* Other:
  * Low-level graph construction now calls the TensorFlow C API. This change should be invisible to most users, but can be disabled by setting the environment variable `TF_C_API_GRAPH_CONSTRUCTION=0` in this release. Future releases will remove the ability to disable this change. Please [file a bug](https://github.com/tensorflow/tensorflow/issues/new) if you find yourself using this escape hatch.
  * Add description of shapes and a pointer to tutorial notebook in `tf.distributions.Distribution`.
  * Update scatter operations:
    * Add `tf.scatter_min` and `tf.scatter_max`
    * Extend scatter operations to work with a scalar update parameter.
  * Move cuDNN RNN ops to core for use in TensorFlow codebase only.
  * Add `float64` support for `Conv2d`, `Conv2dBackpropInput`, and `Conv2dBackpropFilter`.
  * Add `float64` support for `AvgPool`/`AvgPoolGrad`.
  * Make graph name scope thread local so that they work correctly in multi-threaded environments.
  * Update nsync synchronization library to avoid slow primitives on Linux.
  * Removed need to put nsync/public on C include path when building custom ops.
  * Add `tf.image.psnr`, `tf.image.ssim`, `tf.image.ssim_multiscale`, `tf.image.image_gradients`, `tf.image.sobel_edges`.
  * Add links to https://js.tensorflow.org.
  * Fix non-uniformity of orthogonal matrices.
  * Fix bug where multi-image Estimator eval summaries were not displayed correctly.