}
    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);

  output_tensors->reserve(noutputs);
  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),

        absl::StrCat("ZerosLike", ToId(t)).c_str()));
  }
  TF_RETURN_IF_ERROR(op->AddInput(t));
  int num_outputs = 1;
  std::vector<AbstractTensorHandle*> outputs(num_outputs);
  TF_RETURN_IF_ERROR(
      op->Execute(absl::Span<AbstractTensorHandle*>(outputs), &num_outputs));
  *result = outputs[0];
  return absl::OkStatus();
}
}  // namespace

absl::Status GradientRegistry::Register(

  auto outputs = parallel_device.Execute(
      context.get(), {reduced_values.get(), run_collective.get()},
      "AssertAndCollective", TFE_OpGetAttrs(call_op.get()),
      /*expected_max_outputs=*/1, status.get());
  ASSERT_EQ(TF_GetCode(status.get()), TF_OK) << TF_Message(status.get());
  ASSERT_EQ(outputs->size(), 1);
  ParallelTensor* parallel_result = (*outputs)[0].get();

    std::vector<MaybeParallelTensorOwned> outputs;
    outputs.reserve(t->num_tensors());
    for (int i = 0; i < t->num_tensors(); ++i) {
      TensorHandlePtr this_output(
          TFE_TensorHandleCopySharingTensor(t->tensor(i), status));
      outputs.emplace_back(std::move(this_output));
      if (TF_GetCode(status) != TF_OK) return result;
    }
    result.emplace(std::move(outputs));
    return result;
  }

			if !match {
				continue
			}
			for _, child := range children {
				outputs = append(outputs, fmt.Sprintf("%v%v%v -> %v", n, equality, k, child))
			}
		}
	}
	return outputs, len(outputs) > 0
}

// PrintListenerSummary prints a summary of the relevant listeners in the config dump to the ConfigWriter stdout
func (c *ConfigWriter) PrintListenerSummary(filter ListenerFilter) error {

                    final List<String> outputStrings = split(sides.get(1), ",");
                    outputs = new String[outputStrings.size()];
                    for (int i = 0; i < outputs.length; i++) {
                        outputs[i] = unescape(outputStrings.get(i)).trim();
                    }

                    if (inputs.length > 0 && outputs.length > 0) {
                        id++;

  if (TF_GetCode(status) != TF_OK) {
    cancellation_manager_->StartCancel();
    return;
  }
  unwrapped_results.resize(real_num_outputs);
  outputs->reserve(real_num_outputs);
  for (TFE_TensorHandle* unwrapped_result : unwrapped_results) {
    outputs->emplace_back(unwrapped_result);
  }
}

ParallelDevice::ParallelDevice(const std::vector<std::string>& devices,

   *
   *   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();

                return matchOutput;
            }

            pendingOutput = fst.outputs.add(pendingOutput, scratchArc.output());

            if (scratchArc.isFinal()) {
                matchOutput = fst.outputs.add(pendingOutput, scratchArc.nextFinalOutput());
                longestMatchEndOffset = start + index + Character.charCount(codePoint);
            }