TFE_ContextOptions* opts = TFE_NewContextOptions();
  TFE_Context* ctx = TFE_NewContext(opts, status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);
  TFE_DeleteContextOptions(opts);

  TFE_TensorHandle* h = TestScalarTensorHandle(ctx, 1.0f);
  TFE_TensorDebugInfo* debug_info = TFE_TensorHandleTensorDebugInfo(h, status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

    CHECK_EQ(errors::OK, s.code()) << s.message();
  }
};

class PassThroughGradientFunction : public GradientFunction {
 public:
  Status Compute(AbstractContext* ctx,
                 absl::Span<AbstractTensorHandle* const> grad_outputs,
                 absl::Span<AbstractTensorHandle*> grad_inputs) override {
    CHECK_EQ(grad_outputs.size(), 1);
    CHECK_EQ(grad_inputs.size(), 1);

limitations under the License.
==============================================================================*/
#include "tensorflow/c/experimental/gradients/grad_test_helper.h"

#include "tensorflow/c/eager/gradient_checker.h"
#include "tensorflow/c/experimental/gradients/tape/tape_context.h"
#include "tensorflow/core/platform/test.h"

namespace tensorflow {
namespace gradients {
namespace internal {

  auto* counter = CreateCounter0(counter_name);
  auto* reader = TFE_MonitoringNewCounterReader(counter_name);
  IncrementCounter0(counter);

  int64_t actual = TFE_MonitoringReadCounter0(reader);

  CHECK_EQ(actual, 1);
}

TEST(CAPI, MonitoringCellReader1) {
  auto counter_name = "test/counter1";
  auto label_name = "test/label";
  auto* counter = CreateCounter1(counter_name, label_name);

See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#include "tensorflow/c/eager/gradient_checker.h"

#include <memory>

#include "absl/types/span.h"
#include "tensorflow/c/eager/abstract_tensor_handle.h"
#include "tensorflow/c/experimental/ops/math_ops.h"
#include "tensorflow/c/tf_tensor.h"

    t.reset(x_raw);
  }

  AbstractOperationPtr check_numerics_op(ctx->CreateOperation());
  ForwardOperation forward_op;
  Status s = Reset(check_numerics_op.get(), "CheckNumerics",
                   /*raw_device_name=*/nullptr, &forward_op);
  ASSERT_EQ(errors::OK, s.code()) << s.message();
  if (isa<TracingOperation>(check_numerics_op.get())) {
    s = dyn_cast<TracingOperation>(check_numerics_op.get())
            ->SetOpName("check_numerics");

}

// TODO(vnvo2409): Add more tests from `python/ops/gradient_checker_v2_test.py`.
// These tests should not be confused with `[*]_grad_test` which compare the
// result of `gradient_checker` and `[*]_grad`. The tests here test the
// functionality of `gradient_checker` by comparing the result with expected
// manual user-provided gradients.
class GradientCheckerTest

    Status s = StatusFromTF_Status(status.get());
    CHECK_EQ(errors::OK, s.code()) << s.message();
  }

 public:
  bool UseMlir() const { return strcmp(std::get<0>(GetParam()), "mlir") == 0; }
  bool UseFunction() const { return std::get<2>(GetParam()); }
};

// Checks that inputs[0] is a scalar.
Status TestScalarShape(AbstractContext* ctx,

#include "tensorflow/core/platform/test.h"
#include "tensorflow/core/protobuf/tensorflow_server.pb.h"

using ::tensorflow::string;

string MatMulFunction(const string& matmul_device) {
  tensorflow::FunctionDef def;
  CHECK(tensorflow::protobuf::TextFormat::ParseFromString(
      absl::StrCat("    signature {"
                   "      name: 'MatMulFunction'"
                   "      input_arg {"
                   "        name: 'a'"

CheckpointReader::BuildV2VarMaps() {
  CHECK(v2_reader_ != nullptr);
  CHECK(v2_reader_->status().ok());

  // First pass: filters out the entries of the slices.
  std::unordered_set<string> filtered_keys;
  BundleEntryProto entry;
  v2_reader_->Seek(kHeaderEntryKey);
  for (v2_reader_->Next(); v2_reader_->Valid(); v2_reader_->Next()) {
    CHECK(entry.ParseFromArray(v2_reader_->value().data(),