permissions:
  contents: read

jobs:
  build:
    # Don't do this in forks, and if labeled, only for 'kokoro:force-run'
    if: github.repository == 'tensorflow/tensorflow' && (github.event.action != 'labeled' || (github.event.action == 'labeled' && github.event.label.name == 'kokoro:force-run'))
    runs-on: [self-hosted, linux, ARM64]
    strategy:
      matrix:
        pyver: ['3.10']
    steps:

</ul>
<h2>Googlers-Only Links</h2>
<ul>
<li><a href="http://sponge2/$KOKORO_BUILD_ID">Sponge2</a></li>
<li><a href="http://sponge/target:$KOKORO_JOB_NAME">Sponge - recent jobs</a></li>
<li><a href="http://fusion2/ci/kokoro/prod:$(echo "$KOKORO_JOB_NAME" | sed 's!/!%2F!g')">Test Fusion - recent jobs</a></li>
<li><a href="http://cs/f:devtools/kokoro/config/prod/$KOKORO_JOB_NAME">Codesearch - job definition</a></li>

    runs-on: ubuntu-latest
    permissions:
      contents: read
      issues: write
      pull-requests: read
    if: |
      github.repository == 'tensorflow/tensorflow' &&
      startsWith(github.event.head_commit.message, 'Rollback of PR #')
    steps:
      - name: Checkout repo
        uses: actions/checkout@755da8c3cf115ac066823e79a1e1788f8940201b # v3.2.0
      - name: Create a new Github Issue

   
      * Try Google Colab to use TensorFlow.
         * The easiest way to use TF will be to switch to [google colab](https://colab.sandbox.google.com/notebooks/welcome.ipynb#recent=true). You get pre-installed latest stable TF version. Also you can use ```pip install```  to install any other preferred TF version.

    you're running the same code that you're testing:

    *   Use an up to date [tf-nightly](https://pypi.org/project/tf-nightly/)
        `pip install -U tf-nightly`
    *   Rebase your pull request onto a recent pull from
        [TensorFlow's](https://github.com/tensorflow/tensorflow) master branch.

2.  If you are changing the code and the docstring of a class/function/method,
    then you will need to

    TF_CoordinationServiceAgent* agent);

TF_CAPI_EXPORT extern void TF_CoordinationServiceInsertKeyValue(
    const char* key, int64_t key_size, const char* value, int64_t value_size,
    TF_CoordinationServiceAgent* agent, TF_Status* status);

// Obtains key-value from coordination service agent. The returned `TF_Buffer`
// is a newly allocated buffer to hold the string key-value, and caller is

      tensorflow::unwrap(ctx)->GetDistributedManager();
  tsl::CoordinationServiceAgent* coord_agent =
      dist_mgr->GetCoordinationServiceAgent();
  if (coord_agent == nullptr) {
    status->status = tensorflow::errors::FailedPrecondition(
        "Coordination service agent is not enabled.");
    return;
  }
  status->status = coord_agent->InsertKeyValue(key, value);
}

## Some of TensorFlow's code is derived from Caffe, which is subject to the following copyright notice:

COPYRIGHT

All contributions by the University of California:

Copyright (c) 2014, The Regents of the University of California (Regents)
All rights reserved.

All other contributions:

Copyright (c) 2014, the respective contributors
All rights reserved.

        `tf.math.rsqrt`, `tf.math.segment_max`, `tf.math.segment_mean`,
        `tf.math.segment_min`, `tf.math.segment_prod`, `tf.math.segment_sum`,
        `tf.math.sin`, `tf.math.sinh`, `tf.math.softplus`, `tf.math.softsign`,
        `tf.math.squared_difference`, `tf.math.tan`,
        `tf.math.unsorted_segment_max`, `tf.math.unsorted_segment_min`,
        `tf.math.unsorted_segment_prod`, `tf.math.unsorted_segment_sum`,

        "@com_google_absl//absl/strings",
        "@com_google_absl//absl/time",
        "@local_xla//xla/tsl/c:tsl_status_internal",
        "@local_xla//xla/tsl/distributed_runtime/coordination:coordination_service_agent",
    ],
    alwayslink = 1,
)

cc_library(
    name = "c_api_experimental_reader",
    testonly = True,
    srcs = [
        "c_api_experimental_reader.cc",
    ],
    hdrs = [