ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());

  // Build inputs and outputs.
  TF_AbstractTensor* inputs[2] = {at, at};
  TF_OutputList* o = TF_NewOutputList();
  TF_OutputListSetNumOutputs(o, 1, status.get());
  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());

  // Execute.
  TF_ExecuteOperation(op, 2, inputs, o, status.get());

namespace tensorflow {
namespace gradients {

/* Returns numerical grad inside `dtheta_approx` given `forward` model and
 * parameter specified by `input_index`.
 *
 * I.e. if y = <output of the forward model> and w = inputs[input_index],
 * this will calculate dy/dw numerically.
 *
 * `use_function` indicates whether to use graph mode(true) or eager(false).
 *
 * `numerical_grad` is the pointer to the AbstractTensorHandle* which will

            final VaErrorHook hook) {
        return getEntity(form).map(entity -> {
            final String[] newInputs = splitLine(form.inputs);
            validateMappingString(action, newInputs, "inputs", hook);
            entity.setNewInputs(newInputs);
            final String newOutput = form.output;
            entity.setNewOutput(newOutput);
            return entity;
        });
    }

    /** CRUD operation mode (CREATE, EDIT, etc.) */
    @ValidateTypeFailure
    public Integer crudMode;

    /** Input terms (comma-separated) that will be mapped to the output term */
    @Required
    @Size(max = 1000)
    public String inputs;

    /** Output term that input terms will be mapped to */
    @Size(min = 1, max = 1000)
    public String output;

    /**

	testCases := []struct {
		args       []string
		totalSizes []uint64
		indexes    [][]uint64
		success    bool
	}{
		// Invalid inputs.
		{
			[]string{"data{1...17}/export{1...52}"},
			[]uint64{14144},
			nil,
			false,
		},
		// Valid inputs.
		{
			[]string{"data{1...3}"},
			[]uint64{3},
			[][]uint64{{3}},
			true,
		},
		{

            // Process each input from the previous converter
            for (final String input : currentInputs) {
                if (resultSet.size() >= maxReadingNum) {
                    break;
                }

                // Convert the input and collect results
                final List<String> results = converter.convert(input, field, lang);
                if (results == null) {

tdata/huffman-zero.dyn.expect-noinput", "src/compress/flate/testdata/huffman-zero.golden", "src/compress/flate/testdata/huffman-zero.in", "src/compress/flate/testdata/huffman-zero.wb.expect", "src/compress/flate/testdata/huffman-zero.wb.expect-noinput", "src/compress/flate/testdata/null-long-match.dyn.expect-noinput", "src/compress/flate/testdata/null-long-match.wb.expect-noinput", "src/compress/flate/token.go", "src/compress/flate/writer_test.go", "src/compress/gzip/", "src/compress/gzip/example_test.go",...

                  } catch (ExecutionException expected) {
                  }
                  try {
                    peeker.getDone(nonInput);
                    fail("Peeker should not be able to peek into non-input ClosingFuture.");
                  } catch (IllegalArgumentException expected) {
                  }
                  capturedPeeker.set(peeker);

    /** The CRUD operation mode for form processing */
    @ValidateTypeFailure
    public Integer crudMode;

    /** The input terms that should be considered synonymous */
    @Required
    @Size(max = 1000)
    public String inputs;

    /** The output synonyms that should be matched for the input terms */
    @Required
    @Size(max = 1000)
    public String outputs;

    /**

                   const char* raw_device_name, ForwardOperation*);

// Records the inputs in the `ForwardOperation`.
absl::Status AddInput(AbstractOperation*, AbstractTensorHandle*,
                      ForwardOperation*);
absl::Status AddInputList(AbstractOperation*,
                          absl::Span<AbstractTensorHandle* const> inputs,
                          ForwardOperation*);

// Sets the attrs in the `ForwardOperation`.