java.io.File basedir; protected String[] includes; protected String[] excludes; protected java.util.Vector filesIncluded; protected java.util.Vector filesNotIncluded; protected java.util.Vector filesExcluded; protected java.util.Vector dirsIncluded; protected java.util.Vector dirsNotIncluded; protected java.util.Vector dirsExcluded; protected java.util.Vector filesDeselected; protected java.util.Vector dirsDeselected; protected boolean haveSlowResults; protected boolean isCaseSensitive; private boolean...

      }
    }
  }

 private:
  // TODO(b/174778737): Only hold needed inputs.
  vector<AbstractTensorHandle*> forward_inputs_;
};

class DivNoNanGradientFunction : public GradientFunction {
 public:
  explicit DivNoNanGradientFunction(vector<AbstractTensorHandle*> f_inputs,
                                    vector<AbstractTensorHandle*> f_outputs)
      : forward_inputs_(f_inputs), forward_outputs_(f_outputs) {

#include "tensorflow/core/platform/status.h"

using tensorflow::string;

namespace {

std::vector<int64_t> TensorShapeAsVector(const tensorflow::TensorHandle& handle,
                                         tensorflow::Status* status) {
  std::vector<int64_t> shape;
  int rank = -1;
  *status = handle.NumDims(&rank);
  if (!status->ok()) {
    return shape;
  }
  shape.reserve(rank);

	MOVL (AX)(X0*1), AX             // ERROR "invalid instruction"
	MOVL (AX)(Y0*1), AX             // ERROR "invalid instruction"
	// VSIB/VM is invalid without vector index.
	// TODO(quasilyte): improve error message (#21860).
	// "invalid VSIB address (missing vector index)"
	VPGATHERQQ Y2, (BP), Y1         // ERROR "invalid instruction"
	// AVX2GATHER mask/index/dest #UD cases.

    std::vector<TF_Output> out;
    for (auto op : ops) {
      out.push_back({op, 0});
    }
    return out;
  }

  void Define(int num_opers, const std::vector<TF_Operation*>& opers,
              const std::vector<TF_Operation*>& inputs,
              const std::vector<TF_Operation*>& outputs,
              const std::vector<string>& output_names,
              bool expect_failure = false) {

    }

    /**
     * {@link Vector}の新しいインスタンスを作成して返します。
     *
     * @param <E>
     *            {@link Vector}の要素型
     * @return {@link Vector}の新しいインスタンス
     * @see Vector#Vector()
     */
    public static <E> Vector<E> newVector() {
        return new Vector<>();
    }

    /**
     * {@link Vector}の新しいインスタンスを作成して返します。
     *
     * @param <E>

#include "tensorflow/c/tf_status_helper.h"
#include "tensorflow/core/platform/errors.h"
#include "tensorflow/core/platform/test.h"

namespace tensorflow {
namespace gradients {
namespace internal {
namespace {
using std::vector;

class CustomGradientTest
    : public ::testing::TestWithParam<std::tuple<const char*, bool, bool>> {
 protected:
  void SetUp() override {
    TF_StatusPtr status(TF_NewStatus());

// don't want to have a `std::vector<std::string>` at global level, we use a
// static pointer to such a vector: we construct it via `SchemeVector()` below
// and when tests are instantiated we process it using `GetSchemes()`.
static std::vector<std::string>* SchemeVector() {
  static std::vector<std::string>* schemes = new std::vector<std::string>;
  return schemes;
}

    Model model, Model grad_model, AbstractContext* ctx,
    absl::Span<AbstractTensorHandle* const> inputs, bool use_function,
    double abs_error) {
  auto num_inputs = inputs.size();
  std::vector<AbstractTensorHandle*> outputs(num_inputs);
  auto s = RunModel(grad_model, ctx, inputs, absl::MakeSpan(outputs),
                    /*use_function=*/use_function);
  ASSERT_EQ(errors::OK, s.code()) << s.message();

  return SetAttrShape(attr_name, shape.dim_sizes().data(), shape.dims());
}

inline Status AbstractOperation::SetAttrStringList(
    const char* attr_name, absl::Span<string const> values) {
  std::vector<const char*> raw_strs;
  std::vector<size_t> lengths;
  raw_strs.reserve(values.size());
  lengths.reserve(values.size());
  for (const auto& s : values) {
    raw_strs.emplace_back(s.data());
    lengths.emplace_back(s.size());