TF_EXPECT_OK(NodeDefBuilder("identity_op", "Identity")
                   .Input(FakeInput(DT_FLOAT))
                   .Attr("T", DT_FLOAT)
                   .Finalize(node_def()));
  TF_EXPECT_OK(InitOp());
  AddInputFromArray<float>(TensorShape({1, 2}), {6.9, 4.2});
  TF_EXPECT_OK(RunOpKernel());

  XlaCompiler::SingleOpCompileArgument single_op_arg(*context_);

  std::vector<XlaArgument> args(1);

		wantErr            bool
	}{
		{
			name:             "returns expected secret summary onto Stdout",
			callPrime:        true,
			wantOutputSecret: "testdata/secretsummary.txt",
		},
		{
			name:    "errors if config dump is not primed",
			wantErr: true,
		},
		{
			name:               "returns expected workload summary onto Stdout",
			callPrime:          true,
			wantOutputWorkload: "testdata/workloadsummary.txt",
		},

  // CHECK-NEXT: [[PREFIX:%.*]] = tf_mlrt.constop
  %cst = "tf.Const"() {__op_key = 0: i32, value = dense<"restore_ariables"> : tensor<!tf_type.string>} : () -> tensor<!tf_type.string>
  // CHECK-NEXT: [[SLICE:%.*]] = tf_mlrt.constop
  %cst_0 = "tf.Const"()  {__op_key = 1: i32, value = dense<""> : tensor<1x!tf_type.string>} : () -> tensor<1x!tf_type.string>
  // CHECK-NEXT: [[NAME:%.*]] = tf_mlrt.constop

	// Push K elements randomly onto the stacks.
	sum := 0
	for i := 0; i < K; i++ {
		sum += i
		node := allocMyNode(i)
		LFStackPush(stacks[i%2], fromMyNode(node))
	}
	c := make(chan bool, P)
	for p := 0; p < P; p++ {
		go func() {
			r := rand.New(rand.NewSource(rand.Int63()))
			// Pop a node from a random stack, then push it onto a random stack.
			for i := 0; i < N; i++ {

        // Important that the launchables are unpacked on the client side, to avoid sending back any additional internal state that
        // the launchable may hold onto. For example, GradleTask implementations hold onto every task for every project in the build
        List<TaskExecutionRequest> requests = new ArrayList<TaskExecutionRequest>(launchables.size());
        for (InternalLaunchable launchable : launchables) {

		select {
		case b.c <- t:
			b.mu.Unlock()
			return
		default:
		}
	}
	b.backlog = append(b.backlog, t)
	b.mu.Unlock()
}

// Load sends the earliest buffered data, if any, onto the read channel
// returned by Get(). Users are expected to call this every time they read a
// value from the read channel.
func (b *Unbounded[T]) Load() {
	b.mu.Lock()
	if len(b.backlog) > 0 {
		n := new(T)

      auto mul_rhs = ExpandTo4DForConv(gamma_cst);
      broadcasted_gamma = rewriter.create<ConstOp>(loc, mul_rhs);
    } else if (isa<TFL::DepthwiseConv2DOp>(mul_op_lhs)) {
      auto mul_rhs = ExpandTo4DForDepthwiseConv(gamma_cst);
      broadcasted_gamma = rewriter.create<ConstOp>(loc, mul_rhs);
    } else {
      return failure();
    }

 * See the License for the specific language governing permissions and
 * limitations under the License.
 */

package org.gradle.platform.base;

import org.gradle.api.Incubating;

/**
 * A dependency onto a Gradle component.
 */
@Incubating
public interface DependencySpec {

    /**
     * The human friendly name of this dependency.
     *
     * @return human friendly name
     */

    IntegerAttr group_key = rewriter.getI32IntegerAttr(0);

    auto const_group_size = rewriter.create<TF::ConstOp>(
        assign_group->getLoc(), assign_group.getResult(0).getType(),
        group_size);
    auto const_group_key = rewriter.create<TF::ConstOp>(
        assign_group->getLoc(), assign_group.getResult(1).getType(), group_key);

  TensorType zero_point_type = scale_type.clone(rewriter.getI32Type());
  scale = rewriter.create<TF::ConstOp>(
      loc, scale_type,
      DenseFPElementsAttr::get(scale_type,
                               {static_cast<float>(qtype.getScale())}));
  zero_point = rewriter.create<TF::ConstOp>(
      loc, zero_point_type,
      DenseIntElementsAttr::get(zero_point_type,