// attribute already exists with a different value, returns an error.
template <typename ContainerT,
          typename = typename std::enable_if<
              std::is_same<mlir::Type, decltype(*std::declval<ContainerT>()
                                                     .begin())>::value>::type>
Status SetTypeAttribute(absl::string_view name, ContainerT types,
                        AttrValueMap* values) {

  for (int device_index = 0; device_index < num_underlying_devices();
       ++device_index) {
    auto device_value = absl::make_unique<DataType>();
    *device_value = values[device_index];
    std::unique_ptr<TF_Tensor, decltype(&TF_DeleteTensor)> tensor(
        TF_NewTensor(
            datatype_enum, /*dims=*/nullptr, /*num_dims=*/0,
            device_value.release(), sizeof(DataType),
            [](void* data, size_t, void* arg) {

  TFE_DeleteTensorHandle(m);
  TFE_DeleteContext(ctx);
  TF_DeleteStatus(status);
}

TEST(CAPI, TensorHandleNullptr) {
  TFE_TensorHandle* h = nullptr;
  std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(
      TF_NewStatus(), TF_DeleteStatus);

  const char* device_type = TFE_TensorHandleDeviceType(h, status.get());
  ASSERT_EQ(TF_INVALID_ARGUMENT, TF_GetCode(status.get()));

// if their schema is not exposed.
//
// The CEL declaration for objects with XPreserveUnknownFields does not expose unknown fields.
func SchemaDeclType(s Schema, isResourceRoot bool) *apiservercel.DeclType {
	if s == nil {
		return nil
	}
	if s.IsXIntOrString() {
		// schemas using XIntOrString are not required to have a type.

// the created lambda functor class.
template <typename TLambda>
class CallableWrapperUnpackArgs
    : public CallableWrapperUnpackArgs<decltype(&TLambda::operator())> {
 public:
  CallableWrapperUnpackArgs(TLambda fn, const char* name)
      : CallableWrapperUnpackArgs<decltype(&TLambda::operator())>(fn, name) {}
};

// This specialization unpacks the arguments from a normal function pointer.

  }
  // For LLVM style RTTI.
  static bool classof(const AbstractContext* ptr) {
    return ptr->getKind() == kGraph;
  }

 private:
  std::unique_ptr<TF_Graph, decltype(&TF_DeleteGraph)> graph_;
  std::vector<TF_Output> inputs_;
  string name_;
};

static TracingContext* GraphTracingFactory(const char* name, TF_Status* s) {
  return new GraphContext(name);
}

}

void CheckTFE_TensorHandleHasFloats(TFE_TensorHandle* handle,
                                    const std::vector<float>& expected_values) {
  std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> status(
      TF_NewStatus(), TF_DeleteStatus);
  TF_Tensor* t = TFE_TensorHandleResolve(handle, status.get());
  ASSERT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get());

// methods here, as part of making this class a singleton and the only way to
// register/use filesystems.

namespace tensorflow {

using UniquePtrTo_TF_Status =
    ::std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)>;

Status ModularFileSystem::NewRandomAccessFile(
    const std::string& fname, TransactionToken* token,
    std::unique_ptr<RandomAccessFile>* result) {
  if (ops_->new_random_access_file == nullptr)

               expected_max_outputs, *default_cancellation_manager_);
  auto result = Join(expected_output_shapes, status);
  if (TF_GetCode(status) != TF_OK) {
    std::unique_ptr<TF_Status, decltype(&TF_DeleteStatus)> await_status(
        TF_NewStatus(), TF_DeleteStatus);
    // Wait until all pending nodes have completed since they may have a
    // reference to default_cancellation_manager_. We ignore the status return

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