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),

#### Coding style for other languages

*   [Google Java Style Guide](https://google.github.io/styleguide/javaguide.html)
*   [Google JavaScript Style Guide](https://google.github.io/styleguide/jsguide.html)
*   [Google Shell Style Guide](https://google.github.io/styleguide/shellguide.html)
*   [Google Objective-C Style Guide](https://google.github.io/styleguide/objcguide.html)

#### Running sanity check

   
   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/core/distributed_runtime/rpc/eager:grpc_eager_client",
        "@com_google_absl//absl/container:flat_hash_map",
        "@com_google_absl//absl/memory",
        "@com_google_absl//absl/strings",
        "@com_google_absl//absl/time",
        "@local_xla//xla/tsl/c:tsl_status_internal",

        "//tensorflow/core/common_runtime/next_pluggable_device/c:tf_rendezvous_c_api_internal",
        "//tensorflow/core/platform:notification",
        "@com_google_absl//absl/status",
        "@com_google_absl//absl/strings",
        "@local_xla//xla/tsl/c:tsl_status_internal",
    ] + select({
        "//tensorflow:android": [

    "@com_github_cares_cares//:__subpackages__",
    "@com_github_googlecloudplatform_tensorflow_gcp_tools//:__subpackages__",
    "@com_github_grpc_grpc//:__subpackages__",
    "@com_google_absl//:__subpackages__",
    "@com_google_googleapis//:__subpackages__",
    "@com_google_protobuf//:__subpackages__",
    "@com_googlesource_code_re2//:__subpackages__",
    "@compute_library//:__subpackages__",
    "@curl//:__subpackages__",

## 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).