}

// NewCoreGenericConfig returns a new core rest generic config.
func (c *CompletedConfig) NewCoreGenericConfig() *corerest.GenericConfig {
	return &corerest.GenericConfig{
		StorageFactory:              c.Extra.StorageFactory,
		EventTTL:                    c.Extra.EventTTL,
		LoopbackClientConfig:        c.Generic.LoopbackClientConfig,

	//
	// There's a lot of state manipulation performed with shortcuts
	// but we need to make sure the tracer can only observe the
	// start and end states to maintain a coherent model and avoid
	// emitting an event for every single transition.
	trace := traceAcquire()

	if locked {
		// Detach the goroutine from the thread; we'll attach to the goroutine we're

        "//tensorflow/core:test",
        "//tensorflow/core:test_main",
    ],
)

cc_library(
    name = "scalars",
    hdrs = [
        "scalars.h",
    ],
)

tf_cc_test(
    name = "scalars_test",
    size = "small",
    srcs = ["scalars_test.cc"],
    deps = [
        ":scalars",
        "//tensorflow/core:test",
        "//tensorflow/core:test_main",
    ],
)

func.func @resource() {
  // CHECK: tf.SomeOp
  // CHECK-SAME: dtype = !corert.resource
  "tf.SomeOp"() {device = "/device:CPU:0", dtype = !tf_type.resource} : () -> ()
  func.return
}

// CHECK-LABEL: func @variant
func.func @variant(%arg: tensor<!tf_type.variant>) {
  // CHECK: tf.ZerosLike
  // CHECK-SAME: T = !corert.variant

// CHECK: tfrt_fallback_async.createop(%arg0) key(0) device("/device:CPU:0") "tf.ParseExampleV2"()
// CHECK-SAME: Tdense = [f32, f32], dense_shapes = [#corert.shape<>, #corert.shape<>]
// CHECK-SAME: num_sparse = 2 : i64, ragged_split_types = [], ragged_value_types = []
// CHECK-SAME: sparse_types = [!corert.string, i64]}
// CHECK-SAME: num_args(7)

tf_cc_tests(
    name = "all_tests",
    size = "small",
    srcs = glob(["*_test.cc"]),
    deps = [
        ":common",
        "//tensorflow/core:core_cpu",
        "//tensorflow/core:framework",
        "//tensorflow/core:test",
        "//tensorflow/core:test_main",
        "//tensorflow/core:testlib",
        "//tensorflow/core/platform:types",
    ],

        ":calibration_statistics_collector_histogram",
        ":calibration_statistics_collector_min_max",
        ":calibration_statistics_proto_cc",
        "//tensorflow/core:test",
        "//tensorflow/core:test_main",
        "@com_google_googletest//:gtest_main",
    ],
)

tf_kernel_library(
    name = "custom_aggregator_op",
    srcs = ["custom_aggregator_op.cc"],

    The correct place is:

    * In the key `content`, that has as value another JSON object (`dict`) that contains:
        * A key with the media type, e.g. `application/json`, that contains as value another JSON object, that contains:
            * A key `schema`, that has as the value the JSON Schema from the model, here's the correct place.

[CreateCertificateRequest] now correct supports RSA-PSS signature algorithms.

// Trace represents an execution trace for testing.
//
// It does a little bit of work to ensure that the produced trace is valid,
// just for convenience. It mainly tracks batches and batch sizes (so they're
// trivially correct), tracks strings and stacks, and makes sure emitted string
// and stack batches are valid. That last part can be controlled by a few options.
//
// Otherwise, it performs no validation on the trace at all.
type Trace struct {