namespace tensorflow {
namespace {

// The tests are parameterized on:
// - a string representing the tracing implementation: "mlir" or "graphdef".
// - a boolean that when true enables TFRT as the execution engine.
class UnifiedCAPI
    : public ::testing::TestWithParam<std::tuple<const char*, bool>> {
 protected:
  void SetUp() override {
    TF_StatusPtr status(TF_NewStatus());

        `minimum_segment_size`, `maximum_cached_engines`, `use_calibration` and
        `allow_build_at_runtime`.
    *   Added a new parameter called `save_gpu_specific_engines` to the
        `.save()` function inside `TrtGraphConverterV2`. When `False`, the
        `.save()` function won't save any TRT engines that have been built. When
        `True` (default), the original behavior is preserved.

  auto entry = GetFactories().find(name);
  if (entry != GetFactories().end()) {
    default_factory = GetFactories().find(name)->second;
    return absl::OkStatus();
  }
  string msg = absl::StrCat(
      "No tracing engine factory has been registered with the key '", name,
      "' (available: ");
  // Ensure deterministic (sorted) order in the error message
  std::set<string> factories_sorted;
  for (const auto& factory : GetFactories())

  string name_;
};

static TracingContext* GraphTracingFactory(const char* name, TF_Status* s) {
  return new GraphContext(name);
}

// Register the tracing implemented in this file as the default tracing engine.
static bool register_tracing = [] {
  RegisterTracingEngineFactory("graphdef", GraphTracingFactory);
  SetDefaultTracingEngine("graphdef").IgnoreError();
  return true;
}();

}  // namespace graph

-   we have to use the name `__mempcpy'.  */
-#   define mempcpy(dest, src, n) __mempcpy (dest, src, n)
-#  endif
-
-#  if !__GNUC_PREREQ (3, 0) || defined _FORCE_INLINES
-#   if _STRING_ARCH_unaligned
-#    ifndef _FORCE_INLINES
-#     define __mempcpy_args(src) \
-     ((const char *) (src))[0], ((const char *) (src))[2],		      \
-     ((const char *) (src))[4], ((const char *) (src))[6],		      \

[community](https://www.tensorflow.org/community) resources that lets
researchers push the state-of-the-art in ML and developers easily build and
deploy ML-powered applications.

TensorFlow was originally developed by researchers and engineers working within
the Machine Intelligence team at Google Brain to conduct research in machine
learning and neural networks. However, the framework is versatile enough to be
used in other areas as well.