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‎README.md‎

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the graph edges represent the multidimensional data arrays (tensors) that flow
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between them. This flexible architecture enables you to deploy computation to one
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or more CPUs or GPUs in a desktop, server, or mobile device without rewriting
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code. TensorFlow also includes [TensorBoard](https://www.tensorflow.org/programmers_guide/summaries_and_tensorboard), a data visualization toolkit.
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code. TensorFlow also includes [TensorBoard](https://www.tensorflow.org/guide/summaries_and_tensorboard), a data visualization toolkit.
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TensorFlow was originally developed by researchers and engineers
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working on the Google Brain team within Google's Machine Intelligence Research

‎RELEASE.md‎

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# Release 1.9.0
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## Major Features And Improvements
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* Update tf.keras to the Keras 2.1.6 API.
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* Updated docs for `tf.keras`: New Keras-based [get started](http://tensorflow.org/versions/r1.9/get_started),
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and [programmers guide page](http://tensorflow.org/versions/r1.9/programmers_guide/keras).
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* Update `tf.keras` to the Keras 2.1.6 API.
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* Added [`tf.keras.layers.CuDNNGRU`](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/keras/layers/CuDNNGRU) and [`tf.keras.layers.CuDNNLSTM`](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/keras/layers/CuDNNLSTM) layers. [Try it](https://colab.sandbox.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb?linkId=53292082).
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* Adding support of core [feature columns](https://www.tensorflow.org/get_started/feature_columns) and [losses](https://www.tensorflow.org/api_docs/python/tf/losses) to [gradient boosted trees estimators](https://github.com/tensorflow/models/tree/master/official/boosted_trees).
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* The [python interface](https://tensorflow-dot-devsite.googleplex.com/versions/r1.9/api_docs/python/tf/contrib/lite)
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for the [TFLite Optimizing Converter](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/lite/toco/README.md)
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has been expanded, and the command line interface (AKA: `toco`, `tflite_convert`) is once again
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included in the standard `pip` installation.
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* Improved data-loading and text processing with:
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* [`tf.decode_compressed`](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/decode_compressed)
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* [`tf.string_strip`](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/string_strip)
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* [`tf.strings.regex_full_match`](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/strings/regex_full_match)
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* Added experimental support for new pre-made Estimators:
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* [`tf.contrib.estimator.BaselineEstimator`](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/contrib/estimator/BaselineEstimator)
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* [`tf.contrib.estimator.RNNClassifier`](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/contrib/estimator/RNNEstimator)
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* [`tf.contrib.estimator.RNNEstimator`](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/contrib/estimator/RNNClassifier)
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* The [distributions.Bijector](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/contrib/distributions/bijectors/Bijector)
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API supports broadcasting for Bijectors with new API changes.
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## Breaking Chances
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* If you're opening empty variable scopes; replace `variable_scope('', ...)` by
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`variable_scope(tf.get_variable_scope(), ...)`.
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## Bug Fixes and Other Changes
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* `tfe.Network` is deprecated. Please inherit from `tf.keras.Model`.
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* Adding support of core feature columns and losses to gradient boosted trees estimators.
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* The distributions.Bijector API supports broadcasting for Bijectors with new API changes. See [here](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/distributions/bijectors/Bijector) for more details.
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* Layered variable names have changed in the following conditions:
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* Using `tf.keras.layers` with custom variable scopes.
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* Using `tf.layers` in a subclassed `tf.keras.Model` class. See [here](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/layers) for more details
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* Using `tf.layers` in a subclassed `tf.keras.Model` class. See
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[here](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/layers) for more details
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## Breaking Chances
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* If you're opening empty variable scopes; replace `variable_scope`('', ...) by `variable_scope`(`tf.get_variable_scope()`, ...).
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## Bug Fixes and Other Changes
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* `tf.data`:
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* `Dataset.from_generator()` now accepts an `args` list, in order to create nested generators.
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* `Dataset.list_files()` now produces determinstic results when `shuffle=False` or a `seed` is passed.
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## Major Features And Improvements
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* `tf.keras` is now part of the core TensorFlow API.
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* [`tf.data`](http://tensorflow.org/programmers_guide/datasets) is now part of
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* [`tf.data`](http://tensorflow.org/guide/datasets) is now part of
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the core TensorFlow API.
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* The API is now subject to backwards compatibility guarantees.
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* TensorFlow Debugger (tfdbg):
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* Add `eval` command to allow evaluation of arbitrary Python/numpy expressions
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in tfdbg command-line interface. See
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[Debugging TensorFlow Programs](https://www.tensorflow.org/programmers_guide/debugger)
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[Debugging TensorFlow Programs](https://www.tensorflow.org/guide/debugger)
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for more details.
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* Usability improvement: The frequently used tensor filter `has_inf_or_nan` is
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now added to `Session` wrappers and hooks by default. So there is no need
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* Support client-provided ClusterSpec's and propagate them to all workers to enable the creation of dynamic TensorFlow clusters.
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* TensorFlow C library now available for Windows.
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* We released a new open-source version of TensorBoard.
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* [`SavedModel CLI`](https://www.tensorflow.org/versions/master/programmers_guide/saved_model_cli) tool available to inspect and execute MetaGraph in SavedModel
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* [`SavedModel CLI`](https://www.tensorflow.org/versions/master/guide/saved_model_cli) tool available to inspect and execute MetaGraph in SavedModel
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* Android releases of TensorFlow are now pushed to jcenter for easier
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integration into apps. See
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https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/android/README.md

‎tensorflow/contrib/autograph/README.md‎

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AutoGraph is a Python to TensorFlow compiler.
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With AutoGraph, you can write [Eager style](https://www.tensorflow.org/programmers_guide/eager) code in a concise manner, and run it as a TensorFlow graph. AutoGraph uses source code transformation and partial evaluation to generate Python code that builds an equivalent TensorFlow subgraph. The result is code that behaves like ops and can be freely combined with other TensorFlow ops.
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With AutoGraph, you can write [Eager style](https://www.tensorflow.org/guide/eager) code in a concise manner, and run it as a TensorFlow graph. AutoGraph uses source code transformation and partial evaluation to generate Python code that builds an equivalent TensorFlow subgraph. The result is code that behaves like ops and can be freely combined with other TensorFlow ops.
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For example, this Python function:
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‎tensorflow/contrib/data/__init__.py‎

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guarantees as `tf.data`, but we will provide deprecation advice in advance of
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removing existing functionality.
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See the @{$datasets$Importing Data} Programmer's Guide for an overview.
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See @{$guide/datasets$Importing Data} for an overview.
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‎tensorflow/contrib/eager/README.md‎

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For an introduction to eager execution in TensorFlow, see:
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- [User Guide](https://www.tensorflow.org/programmers_guide/eager) ([source](../../docs_src/programmers_guide/eager.md))
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- [User Guide](https://www.tensorflow.org/guide/eager) ([source](../../docs_src/guide/eager.md))
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- Notebook: [Basic Usage](python/examples/notebooks/1_basics.ipynb)
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- Notebook: [Gradients](python/examples/notebooks/2_gradients.ipynb)
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- Notebook: [Importing Data](python/examples/notebooks/3_datasets.ipynb)

‎tensorflow/contrib/eager/python/examples/notebooks/3_datasets.ipynb‎

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"source": [
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"# Eager Execution Tutorial: Importing Data\n",
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"\n",
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"This notebook demonstrates the use of the [`tf.data.Dataset` API](https://www.tensorflow.org/programmers_guide/datasets) to build pipelines to feed data to your program. It covers:\n",
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"This notebook demonstrates the use of the [`tf.data.Dataset` API](https://www.tensorflow.org/guide/datasets) to build pipelines to feed data to your program. It covers:\n",
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"\n",
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"* Creating a `Dataset`.\n",
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"* Iteration over a `Dataset` with eager execution enabled.\n",
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"\n",
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"If you're familiar with TensorFlow graphs, the API for constructing the `Dataset` object remains exactly the same when eager execution is enabled, but the process of iterating over elements of the dataset is slightly simpler.\n",
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"You can use Python iteration over the `tf.data.Dataset` object and do not need to explicitly create an `tf.data.Iterator` object.\n",
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"As a result, the discussion on iterators in the [Programmer's Guide](https://www.tensorflow.org/programmers_guide/datasets) is not relevant when eager execution is enabled."
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"As a result, the discussion on iterators in the [TensorFlow Guide](https://www.tensorflow.org/guide/datasets) is not relevant when eager execution is enabled."
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]
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},
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{
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"source": [
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"# Step 1: Create a source `Dataset`\n",
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"\n",
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"Create a _source_ dataset using one of the factory functions like [`Dataset.from_tensors`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#from_tensors), [`Dataset.from_tensor_slices`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#from_tensor_slices) or using objects that read from files like [`TextLineDataset`](https://www.tensorflow.org/api_docs/python/tf/data/TextLineDataset) or [`TFRecordDataset`](https://www.tensorflow.org/api_docs/python/tf/data/TFRecordDataset). See the [Programmer's Guide](https://www.google.com/url?sa=D\u0026q=https%3A%2F%2Fwww.tensorflow.org%2Fprogrammers_guide%2Fdatasets%23reading_input_data) for more information."
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"Create a _source_ dataset using one of the factory functions like [`Dataset.from_tensors`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#from_tensors), [`Dataset.from_tensor_slices`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#from_tensor_slices) or using objects that read from files like [`TextLineDataset`](https://www.tensorflow.org/api_docs/python/tf/data/TextLineDataset) or [`TFRecordDataset`](https://www.tensorflow.org/api_docs/python/tf/data/TFRecordDataset). See the [TensorFlow Guide](https://www.tensorflow.org/guide/datasets#reading_input_data) for more information."
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},
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{

‎tensorflow/contrib/eager/python/g3doc/guide.md‎

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immediately: concrete values are returned, instead of creating a computational
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graph that is executed later.
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A user guide is available: https://www.tensorflow.org/programmers_guide/eager
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([source file](../../../../docs_src/programmers_guide/eager.md))
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A user guide is available: https://www.tensorflow.org/guide/eager
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([source file](../../../../docs_src/guide/eager.md))
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We welcome feedback through [GitHub issues](https://github.com/tensorflow/tensorflow/labels/comp:eager).
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‎tensorflow/contrib/lite/toco/README.md‎

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Once an application developer has a trained TensorFlow model, TOCO will accept
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[FlatBuffer](https://google.github.io/flatbuffers/) file. TOCO currently supports
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[SavedModels](https://www.tensorflow.org/programmers_guide/saved_model#using_savedmodel_with_estimators)
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[SavedModels](https://www.tensorflow.org/guide/saved_model#using_savedmodel_with_estimators)
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and frozen graphs (models generated via
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[freeze_graph.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/freeze_graph.py)).
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The TensorFlow Lite FlatBuffer file can be shipped to client devices, generally

‎tensorflow/contrib/tpu/python/tpu/tpu_estimator.py‎

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err_msg = ('Input pipeline contains one or more QueueRunners. '
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'It could be slow and not scalable. Please consider '
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'converting your input pipeline to use `tf.data` instead (see '
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'https://www.tensorflow.org/programmers_guide/datasets for '
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'https://www.tensorflow.org/guide/datasets for '
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'instructions.')
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if _WRAP_INPUT_FN_INTO_WHILE_LOOP:
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‎tensorflow/core/protobuf/config.proto‎

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// https://www.tensorflow.org/guide/version_compat.
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Experimental experimental = 9;
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// Everything inside Experimental is subject to change and is not subject
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// https://www.tensorflow.org/guide/version_compat.
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message Experimental {
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string collective_group_leader = 1;
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// https://www.tensorflow.org/guide/version_compat.
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message Experimental {
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// If non-zero, declares that this graph is going to use collective
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// ops and must synchronize step_ids with any other graph with this

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