* </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();

Jacek Ewertowski <******@****.***> 1730145173 +0100

Jacek Ewertowski <******@****.***> 1730145173 +0100

    }
    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);

John Howard <******@****.***> 1717524306 -0700

  for (int i = 0; i < noutputs; ++i) {
    Node* node = outputs[i].oper ? &outputs[i].oper->node : nullptr;
    int idx = outputs[i].index;
    TF_RETURN_WITH_CONTEXT_IF_ERROR(
        fn_body->graph.IsValidOutputTensor(node, idx),
        "Encountered while processing output ", i, " from function '", fn_name,
        "'");
    TF_RETURN_WITH_CONTEXT_IF_ERROR(ValidateNonRefOutput(node, idx),

  int num_dims_out = TF_NumDims(model_out_tensor);
  TF_DeleteTensor(model_out_tensor);

  // If the output is a scalar, then return the scalar output
  if (num_dims_out == 0) {
    outputs[0] = model_out.release();
    return absl::OkStatus();
  }

  // Else, reduce sum the output to get a scalar

  // Will sum all dimensions, so get a Tensor containing [0,...,num_dims_out-1].
  AbstractTensorHandlePtr sum_dims;