size = "small",
    srcs = ["ram_file_block_cache_test.cc"],
    deps = [
        ":ram_file_block_cache",
        "//tensorflow/c:tf_status_internal",
        "//tensorflow/core:lib",
        "//tensorflow/core:test",
        "//tensorflow/core:test_main",
        "//tensorflow/core/platform:blocking_counter",
        "//tensorflow/core/platform/cloud:now_seconds_env",
    ],
)

tf_cc_test(
    name = "gcs_filesystem_test",

		// interfaces and implementations are mutually exclusive,
		// devirtualization can still select the correct callee for
		// each.
		//
		// If they were not mutually exclusive (for example, two Add
		// calls), then we could not definitively select the correct
		// callee.
		val += m.Multiply(42, selectA(i).Add(one(i), 2))
	}
	return val
}

type AddFunc func(int, int) int

The end result is that if this `istio-iptables` wrapper is used, the correct `iptables` binary should be selected dynamically, regardless of whether the context is a pod, the host, a container, or the host filesystem, or what "default" binary is configured in either the container or the host.

        "//tensorflow/core:all_kernels",
        "//tensorflow/core:framework",
        "//tensorflow/core:lib",
        "//tensorflow/core:protos_all_cc",
        "//tensorflow/core:test",
        "//tensorflow/core:test_main",
        "//tensorflow/core/common_runtime:core_cpu_lib",
        "//tensorflow/core/common_runtime/eager:context",
        "//tensorflow/core/common_runtime/eager:core",
    ],
)

// 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)

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

tf_cc_test(
    name = "while_loop_test",
    size = "medium",
    srcs = ["while_loop_test.cc"],
    deps = [
        ":c_api",
        ":c_test_util",
        "//tensorflow/core:lib",
        "//tensorflow/core:test",
        "//tensorflow/core:test_main",
    ],
)

      $resource,
      (TF_SubOp
        (CreateTFReadVariableOp $src_op, $value, $resource),
        $value
      ),
      (CreateConstBoolAttrFalse)
    )
  >;

// This decomposition is only correct inside XLA as it ignores use_locking
// attribute.
def DecomposeResourceApplyGradientDescentOp :
  Pat<
    (TF_ResourceApplyGradientDescentOp:$src_op $resource, $alpha, $delta, BoolAttr:$_),
    (TF_AssignVariableOp

  operations.

  Offline mode really means anticipating a lack of network connectivity, and as
  a result turning off certain services provided by m2 and providing a coherent
  way of predicting and reporting when network-related failures will take place.
  It means warning users that since the network is missing, certain features and

        "@llvm-project//mlir:Transforms",
    ],
)

tf_cc_test(
    name = "tf_saved_model_test",
    srcs = ["ir/tf_saved_model_test.cc"],
    deps = [
        ":tensorflow",
        "//tensorflow/core:test",
        "//tensorflow/core:test_main",
        "//tensorflow/core/platform:test",
        "@llvm-project//mlir:FuncDialect",
        "@llvm-project//mlir:IR",
        "@llvm-project//mlir:Parser",

import spock.lang.Specification

class HttpHeaderAuthSchemeTest extends Specification {
    def "assure scheme name is correct"() {
        when:
        HttpHeaderAuthScheme headerAuthScheme = new HttpHeaderAuthScheme()

        then:
        headerAuthScheme.schemeName == "header"
    }

    def "test only correct credentials are supported"() {
        given:
        HttpHeaderAuthScheme headerAuthScheme = new HttpHeaderAuthScheme()