//  noutputs - number of elements in `outputs` array
//  outputs - array of TF_Outputs that specify the outputs of the function.
//            If `noutputs` is zero (the function returns no outputs), `outputs`
//            can be null. `outputs` can contain the same tensor more than once.
//  output_names - The names of the function's outputs. `output_names` array

    "input": "aa--",
    "output": "aa--"
  },
  {
    "input": "a†--",
    "output": "xn--a---kp0a"
  },
  {
    "input": "ab--c",
    "output": "ab--c"
  },
  {
    "comment": "Label with leading hyphen",
    "input": "-x",
    "output": "-x"
  },
  {
    "input": "-†",
    "output": "xn----xhn"
  },
  {
    "input": "-x.xn--zca",
    "output": "-x.xn--zca"
  },
  {

		if err != nil {
			return nil, err
		}

		// Pick output column names
		if expr.As != "" {
			output, err = output.Set(expr.As, v)
		} else if comp, ok := getLastKeypathComponent(expr.Expression); ok {
			output, err = output.Set(comp, v)
		} else {
			output, err = output.Set(fmt.Sprintf("_%d", i+1), v)
		}
		if err != nil {
			return nil, err
		}
	}

	// 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"]

  }
  return outputs;
}

void TFOutputsFromOutputs(const std::vector<tensorflow::Output>& outputs,
                          TF_Output* tf_outputs) {
  for (int i = 0; i < outputs.size(); i++) {
    tf_outputs[i].oper = ToOperation(outputs[i].node());
    tf_outputs[i].index = outputs[i].index();
  }
}
#endif  // !defined(IS_MOBILE_PLATFORM) && !defined(IS_SLIM_BUILD)

		for len(output) > 0 && (output[0] < want || output[0] != want && len(output[0]) >= 5 && output[0][:5] == want[:5]) {
			if len(output[0]) >= 5 && output[0][:5] == want[:5] {
				t.Errorf("mismatched output:\nhave %s\nwant %s", output[0], want)
				output = output[1:]
				continue Diff
			}
			t.Errorf("unexpected output: %q", output[0])
			output = output[1:]
		}

		case output == "" && testCase.dlURL != "" && testCase.older > 0:
			t.Errorf("Testcase %d: newer release is available but got empty update message!", i+1)
		case output == "" && (testCase.dlURL == "" || testCase.older <= 0):
			// Valid no update message case. No further
			// validation needed.
			continue
		case !strings.Contains(output, line1):

                                "default": "alpha",
                            },
                            "output": {"type": "string", "title": "Output"},
                        },
                        "type": "object",
                        "required": ["output"],
                        "title": "MessageEvent",
                    },
                    "MessageEventType": {

  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),

  }

  void Run(const std::vector<std::pair<TF_Operation*, TF_Tensor*>>& inputs,
           TF_Operation* output, int32_t expected_result) {
    Run(inputs, {{output, 0}}, {expected_result});
  }

  // Run the host graph, which now contains a function and check that
  // outputs are as expected.
  // 'T' stands for 'tensor' since the outputs are tensors, not scalars.
  void RunT(const std::vector<std::pair<TF_Operation*, TF_Tensor*>>& inputs,