import org.checkerframework.checker.nullness.qual.Nullable;

/** Emulation of AggregateFutureState. */
@ElementTypesAreNonnullByDefault
abstract class AggregateFutureState<OutputT extends @Nullable Object>
    extends AbstractFuture.TrustedFuture<OutputT> {
  // Lazily initialized the first time we see an exception; not released until all the input futures

 * </ul>
 */
@GwtCompatible(emulated = true)
@ReflectionSupport(value = ReflectionSupport.Level.FULL)
@ElementTypesAreNonnullByDefault
abstract class AggregateFutureState<OutputT extends @Nullable Object>
    extends AbstractFuture.TrustedFuture<OutputT> {
  // Lazily initialized the first time we see an exception; not released until all the input futures
  // have completed and we have processed them all.

 * </ul>
 */
@GwtCompatible(emulated = true)
@ReflectionSupport(value = ReflectionSupport.Level.FULL)
@ElementTypesAreNonnullByDefault
abstract class AggregateFutureState<OutputT extends @Nullable Object>
    extends AbstractFuture.TrustedFuture<OutputT> {
  // Lazily initialized the first time we see an exception; not released until all the input futures
  // have completed and we have processed them all.

 *
 * @param <InputT> the type of the individual inputs
 * @param <OutputT> the type of the output (i.e. this) future
 */
@GwtCompatible
@ElementTypesAreNonnullByDefault
@SuppressWarnings(
    // Whenever both tests are cheap and functional, it's faster to use &, | instead of &&, ||
    "ShortCircuitBoolean")
abstract class AggregateFuture<InputT extends @Nullable Object, OutputT extends @Nullable Object>
    extends AggregateFutureState<OutputT> {

   *
   *   A =[[0, 1],[1, 0]]
   *   B =[[1, 0],[0, 1]]
   *
   *   output1, output2, output3 = two_adds_and_mm(A, B)
   *
   * We expect outputs:
   *
   *   output1 = [[1, 1],[1, 1]]
   *   output2 = [[2, 0],[0, 2]]
   *   output3 = [[2, 2],[2, 2]]
   *
   */

  // Build eager context.
  TFE_ContextOptions* opts = TFE_NewContextOptions();

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    }
    inputs_.push_back(t->output_);
    *output = tensorflow::down_cast<TracingTensorHandle*>(outputs[0]);
    return absl::OkStatus();
  }

  absl::Status Finalize(OutputList* outputs, AbstractFunction** f) override {
    std::vector<TF_Output> graph_outputs;
    graph_outputs.reserve(outputs->outputs.size());
    for (auto* abstract_output : outputs->outputs) {
      GraphTensor* output = dyn_cast<GraphTensor>(abstract_output);

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