* the <i>count</i> of that element (the terms "frequency" and "multiplicity" are equivalent, but
 * not used in this API). Since the count of an element is represented as an {@code int}, a multiset
 * may never contain more than {@link Integer#MAX_VALUE} occurrences of any one element.
 *
 * <p>{@code Multiset} refines the specifications of several methods from {@code Collection}. It

	"cmd/internal/obj/ppc64"
	"cmd/internal/obj/riscv"
	"cmd/internal/obj/s390x"
	"cmd/internal/obj/wasm"
	"cmd/internal/obj/x86"
	"fmt"
	"strings"
)

// Pseudo-registers whose names are the constant name without the leading R.
const (
	RFP = -(iota + 1)
	RSB
	RSP
	RPC
)

// Arch wraps the link architecture object with more architecture-specific information.
type Arch struct {
	*obj.LinkArch

                        /*
                         * Some pipes may require that the hostname in the tree connect
                         * be the netbios name. So if we have the netbios server name
                         * from the NTLMSSP type 2 message, and the share is IPC$, we
                         * assert that the tree connect path uses the netbios hostname.
                         */

# targets matching the provided query, which start with // or @ but not
# //tensorflow (so it looks for //third_party, //external, etc.), and then
# gathers the list of all packages (i.e. directories) which contain those
# targets.
license_query() {
 bazel cquery --experimental_cc_shared_library "$1" --keep_going \
  | grep -e "^//" -e "^@" \
  | grep -E -v "^//tensorflow" \
  | sed -e 's|:.*||' \
  | sort -u
}

 *       rate when using completely <i>random</i> sample input. The true test of a hash function is
 *       how it performs on representative real-world data, which tends to contain many hidden
 *       patterns and clumps. The goal of a good hash function is to stamp these patterns out as
 *       thoroughly as possible.

 *       rate when using completely <i>random</i> sample input. The true test of a hash function is
 *       how it performs on representative real-world data, which tends to contain many hidden
 *       patterns and clumps. The goal of a good hash function is to stamp these patterns out as
 *       thoroughly as possible.

	if err = xlStorage.MakeVol(context.Background(), "exists-legacy"); err != nil {
		t.Fatalf("Unable to create a volume \"exists-legacy\", %s", err)
	}

	if err = xlStorage.AppendFile(context.Background(), "exists-legacy", "as-file/xl.json", []byte(legacyJSON)); err != nil {
		t.Fatalf("Unable to create a file \"as-file\", %s", err)
	}

	condValues["DurationSeconds"] = []string{strconv.FormatInt(int64(dur.Seconds()), 10)}
	return nil
}

func commonAddServiceAccount(r *http.Request, ldap bool) (context.Context, auth.Credentials, newServiceAccountOpts, madmin.AddServiceAccountReq, string, APIError) {
	ctx := r.Context()

	// Get current object layer instance.
	objectAPI := newObjectLayerFn()
	if objectAPI == nil || globalNotificationSys == nil {

	expectedMap["Access-Control-Expose-Headers"] = []string{
		"Date",
		"Etag",
		"Server",
		"Connection",
		"Accept-Ranges",
		"Content-Range",
		"Content-Encoding",
		"Content-Length",
		"Content-Type",
		"Content-Disposition",
		"Last-Modified",
		"Content-Language",
		"Cache-Control",
		"Retry-After",
		"X-Amz-Bucket-Region",
		"Expires",
		"X-Amz*",
		"X-Amz*",
		"*",
	}

  TF_DeleteBuffer(op_list_buf);
}

class DummyKernel : public tensorflow::OpKernel {
 public:
  explicit DummyKernel(tensorflow::OpKernelConstruction* context)
      : OpKernel(context) {}
  void Compute(tensorflow::OpKernelContext* context) override {}
};

// Test we can query kernels
REGISTER_OP("TestOpWithSingleKernel")
    .Input("a: float")
    .Input("b: float")
    .Output("o: float");