bash -c 'clang-format-12 --style=Google {} | git diff --no-index {} -' \
        | tee $BATS_TEST_TMPDIR/needs_help.txt
    echo "You can use clang-format --style=Google -i <file> to apply changes to a file."
    [[ ! -s $BATS_TEST_TMPDIR/needs_help.txt ]]
}

# Note: this is excluded on the full code base, since any submitted code must
# have passed Google's internal style guidelines.
@test "Check pylint for Python files" {

        "//tensorflow/c/eager:c_api",
        "//tensorflow/c/eager:c_api_experimental",
        "//tensorflow/c/eager:tfe_tensorhandle_internal",
        "@com_google_absl//absl/strings",
        "@com_google_absl//absl/types:optional",
        "@com_google_absl//absl/types:variant",
    ],
)

cc_library(
    name = "parallel_device_lib",
    srcs = [":lib_sources"],
    hdrs = [":lib_headers"],

<li><a href="http://cs/$KOKORO_JOB_NAME">Codesearch - All references to this job</a></li>
</ul>
<h2>Non-Googler Links</h2>
<ul>
<li><a href="https://source.cloud.google.com/results/invocations/$KOKORO_BUILD_ID">ResultStore</a></li>
</ul>
</body></html>

  ASSERT_NO_FATAL_FAILURE(CompareNumericalAndAutodiffGradients(
      BiasAddModel, BuildGradModel(BiasAddModel, registry_),
      immediate_execution_ctx_.get(), {A.get(), Bias.get()}, UseFunction()));
}

#ifdef PLATFORM_GOOGLE
INSTANTIATE_TEST_SUITE_P(
    UnifiedCAPI, CppGradients,
    ::testing::Combine(::testing::Values("graphdef", "mlir"),
                       /*tfrt*/ ::testing::Values(false),

   
   Apparently, your CPU model does not support AVX instruction sets. You can still use TensorFlow with the alternatives given below:
   
      * 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.

## 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).

  security-events: write
  # Only need to read contents
  contents: read

jobs:
  scan-scheduled:
    if: github.repository == 'tensorflow/tensorflow'
    uses: "google/osv-scanner-action/.github/workflows/osv-scanner-reusable.yml@v1.6.2-beta1"
    with:
      scan-args: |-
        --lockfile=requirements.txt:./requirements_lock_3_9.txt

    srcs = ["modular_filesystem_test.cc"],
    linkopts = ["-ldl"],
    tags = [
        "manual",  # Requires DSOs as arguments, eventual setup
        "notap",  # b/139060984, requires implementing modular support for Google filesystem
    ],
    deps = [
        ":modular_filesystem",
        "//tensorflow/core:framework_internal",
        "//tensorflow/core/lib/io:path",
        "//tensorflow/core/platform:env",

You may check how your changes will affect TensorFlow by:

1. Creating a PR and observing the presubmit test results
2. Running the CI scripts locally, as explained below
3. **Google employees only**: Google employees can use an internal-only tool
called "MLCI" that makes testing more convenient: it can execute any full CI job
against a pending change. Search for "MLCI" internally to find it.

              break;
            case "arm.com":
              console.log(await script.filter({github, context, domain}));
              break;
            case "google.com":
              console.log("Googler. No action necessary");
              break;
            default:
              console.log("Skip Trusted Partner Action");