status->status = c.construction_status();
  if (!status->status.ok()) return;

  if (op_reg_data->shape_inference_fn == nullptr) {
    status->status =
        InvalidArgument("No shape inference function exists for op '",
                        node_def.op(), "', did you forget to define it?");
    return;
  }

  status->status = c.Run(op_reg_data->shape_inference_fn);

        TFE_NewOp(context, "Const", status), TFE_DeleteOp);
    if (TF_GetCode(status) != TF_OK) return nullptr;
    TFE_OpSetDevice(const_op.get(), underlying_devices_[device_index].c_str(),
                    status);
    if (TF_GetCode(status) != TF_OK) return nullptr;
    TFE_OpSetAttrTensor(const_op.get(), "value", tensor.get(), status);
    if (TF_GetCode(status) != TF_OK) return nullptr;

	"io"
	"strings"

	"github.com/minio/pkg/v3/wildcard"
)

// State - enabled/disabled/suspended states
// for multifactor and status of versioning.
type State string

// Various supported states
const (
	Enabled State = "Enabled"
	// Disabled  State = "Disabled" only used by MFA Delete not supported yet.
	Suspended State = "Suspended"
)

var (

  ASSERT_EQ(0, TFE_TensorDebugInfoOnDeviceNumDims(debug_info));

  TFE_DeleteTensorDebugInfo(debug_info);
  TFE_DeleteTensorHandle(h);
  TFE_DeleteContext(ctx);
  TF_DeleteStatus(status);
}

TEST(CApiDebug, 2DCPU) {
  TF_Status* status = TF_NewStatus();
  TFE_ContextOptions* opts = TFE_NewContextOptions();
  TFE_Context* ctx = TFE_NewContext(opts, status);
  CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status);

        }).orElse(() -> {
            throwValidationErrorApi(messages -> {
                messages.addErrorsFailedToStartJob(GLOBAL, id);
            });
        });
        return asJson(new ApiResponse().status(Status.OK).result());
    }

    // POST /api/admin/scheduler/{id}/stop
    @Execute(urlPattern = "{}/@word")
    public JsonResponse<ApiResult> post$stop(final String id) {

  op.reset(TFE_NewOp(context.get(), "DestroyResourceOp", status.get()));
  TFE_OpAddInput(op.get(), var_handle, status.get());
  TFE_OpSetDevice(op.get(), name, status.get());
  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());
  num_retvals = 0;
  TFE_Execute(op.get(), nullptr, &num_retvals, status.get());
  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());
}

    TF_Status* out_status) const {
  absl::Status status;
  if (reader_ != nullptr) {
    status = reader_->GetTensor(name, out_tensor);
  } else {
    tensorflow::DataType dtype;
    tensorflow::TensorShape shape;
    status = v2_reader_->LookupDtypeAndShape(name, &dtype, &shape);
    if (status.ok()) {
      out_tensor->reset(new Tensor(dtype, shape));
      status = v2_reader_->Lookup(name, out_tensor->get());

		Rules: []replication.Rule{
			{
				Status:                    replication.Enabled,
				Priority:                  1,
				DeleteMarkerReplication:   replication.DeleteMarkerReplication{Status: replication.Enabled},
				DeleteReplication:         replication.DeleteReplication{Status: replication.Enabled},
				Filter:                    replication.Filter{},

};

int64_t LoggedTensorDim(void* data, int dim_index, TF_Status* status) {
  return TFE_TensorHandleDim(reinterpret_cast<LoggedTensor*>(data)->tensor,
                             dim_index, status);
}

int LoggedTensorNumDims(void* data, TF_Status* status) {
  return TFE_TensorHandleNumDims(reinterpret_cast<LoggedTensor*>(data)->tensor,
                                 status);
}

void LoggedTensorDeallocator(void* data) {

                              status);
    CHECK_EQ(TF_GetCode(status), TF_OK) << TF_Message(status);

    matmul = TFE_NewOp(ctx, "MatMulFunction", status);
    ASSERT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status);
    TFE_OpAddInput(matmul, h0_task0, status);
    ASSERT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status);