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),

    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),

        "//tensorflow/compiler/mlir/tensorflow",
        "//tensorflow/compiler/mlir/tensorflow/c:mlir_c_api_registration",
        "//tensorflow/compiler/mlir/tensorflow:error_util",
        "//tensorflow/compiler/mlir/tensorflow:export_graphdef",
        "//tensorflow/compiler/mlir/tensorflow:mlir_import_options",
        "//tensorflow/compiler/mlir/tensorflow:mlir_roundtrip_flags",

            "//tensorflow/cc:scope_internal",
            "//tensorflow/cc:while_loop",
            "//tensorflow/cc/saved_model:loader_lite",
            "//tensorflow/compiler/mlir/tfr:graph_decompose_pass",
            "//tensorflow/compiler/mlir/tfr:node_expansion_pass",
            "//tensorflow/core:core_cpu",
            "//tensorflow/core:core_cpu_internal",
            "//tensorflow/core:framework",

    Status s = StatusFromTF_Status(status.get());
    CHECK_EQ(errors::OK, s.code()) << s.message();
  }

 public:
  bool UseMlir() const { return strcmp(std::get<0>(GetParam()), "mlir") == 0; }
  bool UseFunction() const { return std::get<2>(GetParam()); }
};

// Checks that inputs[0] is a scalar.
Status TestScalarShape(AbstractContext* ctx,

        "//tensorflow/c/experimental/gradients:not_differentiable",
        "//tensorflow/c/experimental/gradients/tape:tape_context",
        "//tensorflow/c/experimental/ops",
        "//tensorflow/compiler/mlir/tensorflow/c:mlir_c_api_registration",
        "//tensorflow/core:lib",
        "//tensorflow/core:protos_all_cc",
        "//tensorflow/core:test",
        "//tensorflow/core:test_main",
        "//tensorflow/core/lib/llvm_rtti",

*   TF XLA

    *   Add new enum value `MLIR_BRIDGE_ROLLOUT_SAFE_MODE_ENABLED` to
        `tf.config.experimental.mlir_bridge_rollout` to enable a \"safe\" mode.
        This runs the MLIR bridge only when an analysis of the graph only when
        an analysis of the graph determines that it is safe to run.
    *   Add new enum value `MLIR_BRIDGE_ROLLOUT_SAFE_MODE_FALLBACK_ENABLED'

        "//tensorflow/c/eager:gradients_internal",
        "//tensorflow/c/eager:unified_api_testutil",
        "//tensorflow/c/experimental/ops",
        "//tensorflow/compiler/mlir/tensorflow/c:mlir_c_api_registration",
        "//tensorflow/core:test",
        "//tensorflow/core:test_main",
        "//tensorflow/core/platform:errors",
    ],
)

filegroup(
    name = "pywrap_required_hdrs",

build:no_tfrt --deleted_packages=tensorflow/compiler/mlir/tfrt,tensorflow/compiler/mlir/tfrt/benchmarks,tensorflow/compiler/mlir/tfrt/ir,tensorflow/compiler/mlir/tfrt/ir/mlrt,tensorflow/compiler/mlir/tfrt/jit/python_binding,tensorflow/compiler/mlir/tfrt/jit/transforms,tensorflow/compiler/mlir/tfrt/python_tests,tensorflow/compiler/mlir/tfrt/tests,tensorflow/compiler/mlir/tfrt/tests/ifrt,tensorflow/compiler/mlir/tfrt/tests/mlrt,tensorflow/compiler/mlir/tfrt/tests/ir,tensorflow/com...

namespace tensorflow {

class FunctionRecord;

// A traced function: this hides the complexity of converting the serialized
// representation between various supported formats e.g. FunctionDef and Mlir
// function.
class AbstractFunction : public core::RefCounted {
 protected:
  enum AbstractFunctionKind { kGraph, kMlir };
  explicit AbstractFunction(AbstractFunctionKind kind) : kind_(kind) {}

 public: