for (int i = 0; i < noutputs; ++i) {
      output_names_vec.push_back(string(output_names[i]));
    }
  }

  // Process control output names.
  std::vector<string> control_output_names_vec;
  if (control_output_names) {
    control_output_names_vec.reserve(ncontrol_outputs);
    for (int i = 0; i < ncontrol_outputs; ++i) {
      control_output_names_vec.push_back(string(control_output_names[i]));
    }
  }

                                    vector<AbstractTensorHandle*> f_outputs)
      : forward_inputs_(f_inputs), forward_outputs_(f_outputs) {
    for (auto input : forward_inputs_) {
      if (input) {
        input->Ref();
      }
    }
    for (auto output : forward_outputs_) {
      if (output) {
        output->Ref();
      }
    }
  }

  Status Compute(AbstractContext* ctx,

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

  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) {

  sources.reserve(output_tensors.size());
  for (const TapeTensor& output_tensor : output_tensors) {
    // Ownership of `aid` transferred to CallBackwardFunction below.
    Gradient* aid;
    if (output_tensor.GetDType() == tensorflow::DT_VARIANT) {
      // Note: Needs to be zeros rather than ones since there's currently no
      // ones_like for variants.
      aid = output_tensor.ZerosLike();
    } else {

  VLOG(1) << "Running the dequeue op";
  TF_Output output{dequeue_op, 0};
  TF_Tensor* ret;
  TF_SessionRun(session, /*run_options*/ nullptr,
                // input related parameters
                /*inputs*/ nullptr, /*input_values*/ nullptr, /*ninputs*/ 0,
                // output related parameters
                /*outputs*/ &output, /*output_values*/ &ret,
                /*noutputs*/ 1,

				return configWriter.PrintClusterDump(filter, outputFormat)
			default:
				return fmt.Errorf("output format %q not supported", outputFormat)
			}
		},
		ValidArgsFunction: completion.ValidPodsNameArgs(ctx),
	}

	clusterConfigCmd.PersistentFlags().StringVarP(&outputFormat, "output", "o", summaryOutput, "Output format: one of json|yaml|short")

	}

	fErr := cmd.Execute()
	output := out.String()

	if c.expectedException {
		if fErr == nil {
			t.Fatalf("Wanted an exception, "+
				"didn't get one, output was %q", output)
		}
	} else {
		if fErr != nil {
			t.Fatalf("Unwanted exception: %v", fErr)
		}
	}

	if c.expectedOutput != "" && c.expectedOutput != output {
		assert.Equal(t, c.expectedOutput, output)

	// Output: true
}

func ExampleFields() {
	fmt.Printf("Fields are: %q", bytes.Fields([]byte("  foo bar  baz   ")))
	// Output: Fields are: ["foo" "bar" "baz"]
}

func ExampleFieldsFunc() {
	f := func(c rune) bool {
		return !unicode.IsLetter(c) && !unicode.IsNumber(c)
	}
	fmt.Printf("Fields are: %q", bytes.FieldsFunc([]byte("  foo1;bar2,baz3..."), f))
	// Output: Fields are: ["foo1" "bar2" "baz3"]

     *           (a module existing only in test output, not in main output) should be uncommon.</li>
     *     </ul>
     *   </li>
     *   <li>Otherwise (test output contains no module information), then:
     *     <ul>
     *       <li>If the main output is on the module-path, place the test output
     *           on a {@code --patch-module} option.</li>

        components["schemas"] = {k: definitions[k] for k in sorted(definitions)}
    if components:
        output["components"] = components
    output["paths"] = paths
    if webhook_paths:
        output["webhooks"] = webhook_paths
    if tags:
        output["tags"] = tags