}
}

void CheckTensorValue(AbstractTensorHandle* t, absl::Span<const float> manuals,
                      absl::Span<const int64_t> dims, double abs_error) {
  TF_Tensor* analytical_tensor;
  auto s = GetValue(t, &analytical_tensor);
  ASSERT_EQ(errors::OK, s.code()) << s.message();

  int64_t num_elem_analytical = 1;
  auto num_dims_analytical = TF_NumDims(analytical_tensor);
  ASSERT_EQ(dims.size(), num_dims_analytical);

      .def("get", &mlir::UnrankedTensorType::get);

  py::class_<mlir::RankedTensorType, mlir::Type>(m, "RankedTensorType")
      .def("get", [](std::vector<int64_t> shape, mlir::Type ty) {
        return mlir::RankedTensorType::get(mlir::ArrayRef<int64_t>(shape), ty);
      });

    TF_ShapeHandle* result, TF_Status* status);

// Places a handle to the ith dimension of the given shape into *result.
TF_CAPI_EXPORT extern void TF_ShapeInferenceContextDim(
    TF_ShapeInferenceContext* ctx, TF_ShapeHandle* shape_handle, int64_t i,
    TF_DimensionHandle* result);

// Returns in <*result> a sub-shape of <shape_handle>, with dimensions

ResourceHandleValueAndId GetResourceHandleValueAndIdBase(
    llvm::StringRef container, llvm::StringRef shared_name,
    llvm::StringRef device, Value resource,
    llvm::SmallDenseMap<ResourceHandle, int64_t> &resource_handle_id_map,
    int64_t &next_id) {
  // Always create a new ID for anonymous handle.
  if (IsResourceHandleAnonymous(shared_name)) return {resource, next_id++};

  virtual Status SetAttrInt(const char* attr_name, int64_t value) = 0;
  virtual Status SetAttrFloat(const char* attr_name, float value) = 0;
  virtual Status SetAttrBool(const char* attr_name, bool value) = 0;
  virtual Status SetAttrType(const char* attr_name, DataType value) = 0;
  virtual Status SetAttrShape(const char* attr_name, const int64_t* dims,
                              const int num_dims) = 0;

  AddInputFromArray<float>(TensorShape({}), {1.f});
  AddInputFromArray<float>(TensorShape({}), {5.f});
  AddInputFromArray<int64_t>(TensorShape({0}), {});
  AddInputFromArray<float>(TensorShape({}), {1.f});
  AddInputFromArray<float>(TensorShape({}), {5.f});
  AddInputFromArray<int64_t>(TensorShape({8}), {1, 4, 6, 7, 3, 2, 1, 0});

  TF_CHECK_OK(RunOpKernel());
  kernel_.reset();

  typedef StablehloConvolutionOptions TableType;
  std::vector<int64_t> window_strides{};
  std::vector<int64_t> padding{};
  std::vector<int64_t> lhs_dilation{};
  std::vector<int64_t> rhs_dilation{};
  std::vector<bool> window_reversal{};
  int64_t input_batch_dimension = 0;
  int64_t input_feature_dimension = 0;
  std::vector<int64_t> input_spatial_dimensions{};
  int64_t kernel_input_feature_dimension = 0;

                     const TF::ResourceAliasAnalysis::Info& analysis) {
    int64_t next_id = 0;
    llvm::SmallDenseMap<Value, int64_t, 8> ids;

    auto assign_id = [&](Value value) {
      if (ids.find(value) == ids.end()) ids.insert({value, next_id++});
    };

    auto get_id = [&](Value value) -> int64_t {
      auto it = ids.find(value);
      assert(it != ids.end());
      return it->second;
    };

  Output slice = ops::Slice(root.WithOpName("slice"), input, begin, size);

  std::unique_ptr<Graph> result;
  TF_ASSERT_OK(IncreaseDynamismForAutoJit(root, &result));

  const int64_t zero_64 = 0;
  const int32_t zero_32 = 0;
  const int64_t one_64 = 1;

  auto m_input = Out(NodeWith(Op("Placeholder"), Name("input")));
  auto m_begin_s64 = Out(NodeWith(
      Op("Cast"), Inputs(Out(NodeWith(Op("Placeholder"), Name("begin"))))));

// tfl.conv_2d / tfl.depthwise_conv_2d / tfl.fully_connected
class CpuConvOp : public TargetHardwareOperation {
  double GetOpCost(mlir::Operation* op) const override {
    float cost = 0.0;
    int64_t arithmetic_count;
    if (ArithmeticCountUtilHelper::GetArithmeticCountForConvAndFullyconnectedOp(
            op, &arithmetic_count)) {
      cost = arithmetic_count * kCPUArithmeticUnitCost;
    } else {