EXPECT_TRUE(TFE_CancellationManagerRegisterCallback(c_mgr, token2, &callback2,
                                                      "callback2"));

  TFE_CancellationToken token3 = TFE_CancellationManagerGetToken(c_mgr);
  EXPECT_TRUE(TFE_CancellationManagerRegisterCallback(c_mgr, token3, &callback1,
                                                      "callback3"));

using tensorflow::TF_StatusPtr;

namespace tensorflow {
namespace {

// The tests are parameterized on:
// - a string representing the tracing implementation: "mlir" or "graphdef".
// - a boolean that when true enables TFRT as the execution engine.
class UnifiedCAPI
    : public ::testing::TestWithParam<std::tuple<const char*, bool>> {
 protected:
  void SetUp() override {
    TF_StatusPtr status(TF_NewStatus());

    TF_GraphProperties* graph_properties);

// Infer tensor shapes through abstract interpretation.
// If assume_valid_feeds is true, it can help infer shapes in the fanout of fed
// nodes. This may cause incorrectness in graph analyses, but is useful for
// simulation or scheduling.
// If aggressive_shape_inference is true, nodes are executed on the host to
// identify output values when possible and does other aggressive strategies.

      [gcs_file](const std::string& dir, GcsFileStat* stat, TF_Status* status) {
        auto children =
            GetChildrenBounded(gcs_file, dir, 1, true, true, status);
        if (TF_GetCode(status) != TF_OK) return;
        if (!children.empty()) {
          stat->base = {0, 0, true};
          return TF_SetStatus(status, TF_OK, "");
        } else {
          return TF_SetStatus(status, TF_INVALID_ARGUMENT, "Not a directory!");

  # Select lines unique to expected_licenses, i.e. missing licenses
  comm -2 -3 $BATS_TEST_TMPDIR/expected_licenses $BATS_TEST_TMPDIR/actual_licenses | grep -v -f $BATS_TEST_TMPDIR/allowed_to_be_missing > $BATS_TEST_TMPDIR/actual_missing_licenses || true

  if [[ -s $BATS_TEST_TMPDIR/actual_extra_licenses ]]; then

                                /*transpose_a = */ true,
                                /*transpose_b = */ true, name_grad_A.c_str()));

      TF_RETURN_IF_ERROR(MatMul(ctx, upstream_grad, A.get(), &matmul_B_output,
                                /*transpose_a = */ true,
                                /*transpose_b = */ true, name_grad_B.c_str()));
    }

    // Gradient for A

bool IsValidStrideCompactRowMajorData(int64_t* shape_arr, int64_t* stride_arr,
                                      int ndim) {
  bool valid = true;
  int64_t expected_stride = 1;
  for (int i = ndim - 1; i >= 0; --i) {
    // Empty tensors are always compact regardless of strides.
    if (shape_arr[i] == 0) return true;
    // Note that dimensions with size=1 can have any stride.
    if (shape_arr[i] != 1 && stride_arr[i] != expected_stride) {

// Creates a new eager Executor. Nodes in one executor are guaranteed to be
// executed in sequence. Assigning nodes to different executors allows executing
// nodes in parallel.
// in_flight_nodes_limit: when is_async is true, this value controls the
// maximum number of in flight async nodes. Enqueuing of additional async ops
// after the limit is reached blocks until some inflight nodes finishes.

  server_def.set_task_index(1);

  std::unique_ptr<tensorflow::GrpcServer> worker_server;
  ASSERT_TRUE(tensorflow::GrpcServer::Create(
                  server_def, tensorflow::Env::Default(), &worker_server)
                  .ok());
  ASSERT_TRUE(worker_server->Start().ok());

  TF_Status* status = TF_NewStatus();
  TFE_ContextOptions* opts = TFE_NewContextOptions();

                                   TensorShape::MaxDimensions(), ".");
  }
  TensorShapeProto proto;
  if (num_dims < 0) {
    proto.set_unknown_rank(true);
  } else {
    for (int d = 0; d < num_dims; ++d) {
      proto.add_dim()->set_size(dims[d]);
    }
  }

  forward_op_->attrs.Set(attr_name, proto);
  return op_->SetAttrShape(attr_name, dims, num_dims);