}

@test "No duplicate files on Windows" {
    cat <<EOF
Please rename files so there are no repeats. For example, README.md and
Readme.md would be the same file on Windows. In this test, you would get a
warning for "readme.md" because it makes everything lowercase. There are
repeats of these filename(s) with different casing:
EOF
    find . | tr '[A-Z]' '[a-z]' | sort | uniq -d | tee $BATS_FILE_TMPDIR/repeats

build:ios --noenable_platform_specific_config

# Suppress all C++ compiler warnings, otherwise build logs become 10s of MBs.
build:android --copt=-w
build:ios --copt=-w
build:linux --host_copt=-w
build:macos --copt=-w
build:windows --copt=/W0
build:windows --host_copt=/W0

# Suppress most C++ compiler warnings to reduce log size but allow
# for specific warnings to still be present.
build:linux --copt="-Wno-all"

        optimizer.
    *   **Learning rate schedule access.** When using a `LearningRateSchedule`,
        The new optimizer's `learning_rate` property returns the
        current learning rate value instead of a `LearningRateSchedule` object
        as before. If you need to access the `LearningRateSchedule` object,
        please use `optimizer._learning_rate`.

  /// and any buffers need to be allocated through filesystem allocation API.
  /// Filesystems may choose to ignore configuration errors but should at least
  /// display a warning or error message to warn the users.
  ///
  /// Plugins:
  ///   * Must set `status` to `TF_OK` if options are updated.
  ///   * Might use any other error value for `status` to signal other errors.
  ///

        token: ${{ secrets.JENKINS_TOKEN }}
        base: ${{ github.event.inputs.release_branch }}
        branch: ${{ github.event.inputs.release_branch }}-${{ steps.cherrypick.outputs.SHORTSHA }}
        reviewers: learning-to-play
        body: |

// and set the device name. It's effectively `TFE_OpSetDevice`, but it is faster
// than separately calling it because if the existing op has the same
// `raw_device_name`, it skips parsing and just leave as it is.
TF_CAPI_EXPORT extern void TFE_OpReset(TFE_Op* op_to_reset,
                                       const char* op_or_function_name,
                                       const char* raw_device_name,

# Tests

tf_cuda_library(
    name = "c_test_util",
    testonly = 1,
    srcs = ["c_test_util.cc"],
    hdrs = ["c_test_util.h"],
    visibility = [
        "//learning/brain:__subpackages__",
        "//tensorflow:__subpackages__",
    ],
    deps = [
        ":c_api",
        ":c_api_experimental",
        "//tensorflow/core:lib",
        "//tensorflow/core:protos_all_cc",

## TensorFlow models are programs

TensorFlow
[**models**](https://developers.google.com/machine-learning/glossary/#model) (to
use a term commonly used by machine learning practitioners) are expressed as
programs that TensorFlow executes. TensorFlow programs are encoded as
computation
[**graphs**](https://developers.google.com/machine-learning/glossary/#graph).
Since models are practically programs that TensorFlow executes, using untrusted

{
    "description": "TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML-powered applications.",
    "license": "Apache-2.0",
    "title": "TensorFlow",
    "upload_type": "software",
    "creators": [
        {
            "name": "TensorFlow Developers"
        }

# TODO(b/173549186): Move Google-internal TF code out of learning/brain
package_group(
    name = "internal",
    packages = [
        "//devtools/python/indexer/...",
        "//learning/brain/keras/...",
        "//learning/brain/mlir/...",
        "//learning/brain/tfrt/...",
        "//learning/lib/ami/simple_ml/...",
        "//learning/pathways/...",
        "//learning/serving/contrib/tfrt/mlir/canonical_ops/...",