if (dst->dims() != 0) {
      return InvalidArgument(
          "Malformed TF_RESOURCE tensor: expected a scalar, got a tensor with "
          "shape ",
          dst->shape().DebugString());
    }
    *dst = tensorflow::Tensor(tensorflow::DT_RESOURCE, dst->shape());
    if (!dst->scalar<tensorflow::ResourceHandle>()().ParseFromString(
            string(static_cast<const char*>(tensor_interface->Data()),

  TF_ImportGraphDefOptionsAddInputMapping(opts, "scalar", 0, {scalar, 0});
  TF_ImportGraphDefOptionsAddReturnOutput(opts, "feed", 0);
  TF_ImportGraphDefOptionsAddReturnOutput(opts, "scalar", 0);
  EXPECT_EQ(2, TF_ImportGraphDefOptionsNumReturnOutputs(opts));
  TF_ImportGraphDefOptionsAddReturnOperation(opts, "scalar");
  EXPECT_EQ(1, TF_ImportGraphDefOptionsNumReturnOperations(opts));

  int num_dims_out = TF_NumDims(model_out_tensor);
  TF_DeleteTensor(model_out_tensor);

  // If the output is a scalar, then return the scalar output
  if (num_dims_out == 0) {
    outputs[0] = model_out.release();
    return absl::OkStatus();
  }

  // Else, reduce sum the output to get a scalar

  // Will sum all dimensions, so get a Tensor containing [0,...,num_dims_out-1].
  AbstractTensorHandlePtr sum_dims;
  {

  const std::vector<int64_t>* shape;
  TF_ASSERT_OK(unknown_length_vector->Shape(&shape));
  EXPECT_THAT(*shape, ElementsAre(-1));

  TensorHandlePtr scalar = FloatTensorHandle(2., status.get());
  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());
  two_vector = VectorFloatTensorHandle({3., 4.}, status.get());

    ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());
    delete to_delete;
  };
  std::unique_ptr<Variable, decltype(variable_deleter)> variable(
      Variable::Create(context, TF_FLOAT, /* Scalar */ {}, 0, device_name,
                       status.get()),
      variable_deleter);
  ASSERT_TRUE(TF_GetCode(status.get()) == TF_OK) << TF_Message(status.get());

      found_shape = true;
    }
  }
  return found_dtype && found_shape;
}

bool IsScalarConst(const tensorflow::NodeDef& node_def, int v) {
  if (node_def.op() != "Const" || node_def.name() != "scalar") {
    return false;
  }
  bool found_dtype = false;
  bool found_value = false;
  for (const auto& attr : node_def.attr()) {
    if (attr.first == "dtype") {

  }

 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,
                       absl::Span<AbstractTensorHandle* const> inputs,
                       absl::Span<AbstractTensorHandle*> outputs) {
  PartialTensorShape shape;

   *                   v
   */
  // Define
  TF_Operation* c = ScalarConst(10, func_graph_, s_, "scalar10");
  Define(-1, {}, {}, {c}, {});

  // Use, run, and verify
  TF_Operation* func_op = Use({});
  Run({}, func_op, 10);
  VerifyFDef({"scalar10_0"}, {}, {{"scalar10", DT_INT32}},
             {{"scalar10_0:output:0", "scalar10"}}, {});
}

TEST_F(CApiFunctionTest, OneOp_OneInput_OneOutput) {
  /*

  TFE_OpSetAttrType(fill_op, "T", TF_FLOAT);
  TFE_OpSetAttrType(fill_op, "Tshape", TF_INT32);

  float five = 5.0;
  TFE_TensorHandle* scalar = TestScalarTensorHandle(tfe_context_, five);
  TF_Tensor* scalarTensor = TFE_TensorHandleResolve(scalar, status_);
  CHECK_EQ(TF_OK, TF_GetCode(status_)) << TF_Message(status_);
  CheckOutputShapes(fill_op,