UnifiedCAPI, CppGradients,
    ::testing::Combine(::testing::Values("graphdef", "mlir"),
                       /*tfrt*/ ::testing::Values(false),
                       /*use_function*/ ::testing::Values(true, false)));
#else
INSTANTIATE_TEST_SUITE_P(
    UnifiedCAPI, CppGradients,
    ::testing::Combine(::testing::Values("graphdef", "mlir"),
                       /*tfrt*/ ::testing::Values(false),

## Wheel Test

This directory is dedicated to tests that require a built TensorFlow wheel
file for testing, such as:

* Ensuring the entire API is importable
* Testing downstream projects against the wheel

Ensure you have Bazel installed and accessible from your command line.

These tests use hermetic Python. They also require a built TensorFlow wheel file
and a requirements_lock file. The requirements_lock file is generated by the

build:sigbuild_remote_cache --remote_cache="https://storage.googleapis.com/tensorflow-devinfra-bazel-cache/manylinux2014" --remote_upload_local_results=false
# Change the value of CACHEBUSTER when upgrading the toolchain, or when testing
# different compilation methods. E.g. for a PR to test a new CUDA version, set
# the CACHEBUSTER to the PR number.
build --action_env=CACHEBUSTER=20220325

# Use Python 3.X as installed in container image

==============================================================================*/
// Classes and utilities that work with Graph C API for internal use.
// This includes functions used for optimizer registration and interfaces needed
// for testing.

#ifndef TENSORFLOW_C_EXPERIMENTAL_GRAPPLER_GRAPPLER_INTERNAL_H_
#define TENSORFLOW_C_EXPERIMENTAL_GRAPPLER_GRAPPLER_INTERNAL_H_

#include <functional>
#include <memory>
#include <unordered_map>

build:sigbuild_remote_cache --remote_cache="https://storage.googleapis.com/tensorflow-devinfra-bazel-cache/manylinux2014" --remote_upload_local_results=false
# Change the value of CACHEBUSTER when upgrading the toolchain, or when testing
# different compilation methods. E.g. for a PR to test a new CUDA version, set
# the CACHEBUSTER to the PR number.
build --action_env=CACHEBUSTER=20220325

# Use Python 3.X as installed in container image

// fixture to create new directories in `SetUp`. Each directory will reside in
// `::testing::TempDir()`, will use a RNG component and the test name. This
// ensures that two consecutive runs are unlikely to clash.
class ModularFileSystemTest : public ::testing::TestWithParam<std::string> {
 public:
  // Initializes `root_dir_` to a unique value made of `::testing::TempDir()`, a
  // static random value unique for all the tests in one invocation, and the

<li><a href="http://cs/f:devtools/kokoro/config/prod/$KOKORO_JOB_NAME">Codesearch - job definition</a></li>
<li><a href="http://cs/f:learning/brain/testing/kokoro/$(echo "$KOKORO_JOB_NAME" | sed 's!tensorflow/!!g')">Codesearch - build definition & scripts</a></li>
<li><a href="http://cs/$KOKORO_JOB_NAME">Codesearch - All references to this job</a></li>
</ul>
<h2>Non-Googler Links</h2>

// - graphdef and MLIR tracing engine
INSTANTIATE_TEST_SUITE_P(Tracing, UnifiedCAPI,
                         ::testing::Combine(::testing::Values("graphdef",
                                                              "mlir"),
                                            ::testing::Values(false)));

}  // namespace

  server_def.set_task_index(0);
  auto cluster = server_def.mutable_cluster();
  auto client_job = cluster->add_job();
  client_job->set_name("localhost");
  int client_port = tensorflow::testing::PickUnusedPortOrDie();
  client_job->mutable_tasks()->insert(
      {0, strings::StrCat("localhost:", client_port)});
  server_def.set_job_name("localhost");
  auto serialized = server_def.SerializeAsString();

#include "tensorflow/core/protobuf/config.pb.h"
#include "tensorflow/core/protobuf/rewriter_config.pb.h"
#include "tensorflow/core/protobuf/tensorflow_server.pb.h"

using tensorflow::string;

namespace {

void BM_InitOp(::testing::benchmark::State& state) {
  TF_Status* status = TF_NewStatus();
  TFE_ContextOptions* opts = TFE_NewContextOptions();
  TFE_Context* ctx = TFE_NewContext(opts, status);