// First create a simple identity function.
  TF_Graph* function_graph = TF_NewGraph();
  TF_OperationDescription* arg_descr =
      TF_NewOperation(function_graph, "Placeholder", "arg");
  TF_SetAttrType(arg_descr, "dtype", TF_INT32);
  TF_Status* status = TF_NewStatus();
  TF_Operation* arg = TF_FinishOperation(arg_descr, status);
  ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status);
  TF_OperationDescription* id_descr =

    TF_Status* status);

// Allocates a temporary Tensor of the specified type and shape. The
// Tensor must not be used after kernel construction is
// complete.
//
// num_dims must equal the size of array dims
TF_CAPI_EXPORT extern TF_Tensor* TF_AllocateTemp(
    TF_OpKernelContext* context, TF_DataType dtype, const int64_t* dims,
    int num_dims, TF_AllocatorAttributes* alloc_attrs, TF_Status* status);

// Specify this trait if the op requires all outputs to have the same type and
// the inputs either have the same type as result or a ref type corresponding to
// the result type.
def TF_OperandsSameAsResultsTypeOrRef : NativeOpTrait<
  "TF::OperandsSameAsResultsTypeOrRef">;

// Op has the same operand and result element types (or type itself, if scalar)
// after resolving reference types (i.e., after converting reference types to

    ),
)

tf_cc_binary(
    name = "bef_executor",
    testonly = True,
    visibility = [":friends"],
    deps = [
        "@tf_runtime//:dtype",
        "@tf_runtime//:simple_tracing_sink",
        "@tf_runtime//tools:bef_executor_expensive_kernels",
        "@tf_runtime//tools:bef_executor_lib",
        "@tf_runtime//tools:bef_executor_lightweight_kernels",

      gtl::ArraySlice<AbstractTensorHandle*> output_gradients,
      absl::Span<AbstractTensorHandle*> result) const override;

  // Builds a tensor filled with ones with the same shape and dtype as `t`.
  Status BuildOnesLike(const TapeTensor& t,
                       AbstractTensorHandle** result) const override;

  // Looks up the ID of a Gradient.

  // Summarizes the value of this tensor. The caller takes ownership of the
  // returned buffer. If `status` is not TF_OK, instead returns a null pointer.
  //
  // Does not include the shape and dtype of the tensor (which is generally
  // appended later), but should include any information specific to this custom
  // device which would be useful for debugging.
  //

}

// CHECK-LABEL: func @tfConst
func.func @tfConst() -> (tensor<4xf32>, tensor<1x1x6x2xf32>) {

ElementsAttr GetTensorValueAsElementsAttr(
    TF::VarHandleOp var_handle_op, const tensorflow::Tensor& resource_tensor,
    const tensorflow::DeviceMgr* mgr, OpBuilder builder) {
  if (resource_tensor.dtype() != tensorflow::DT_RESOURCE) {
    return GetTensorValueAsElementsAttr(resource_tensor, builder);
  }

  auto handle = resource_tensor.scalar<tensorflow::ResourceHandle>()();

    return false;
  }

  if (node.type_string() == "Const") {
    const AttrValue* attr = node.attrs().Find("dtype");
    if (!op_filter_.allow_string_consts && attr != nullptr &&
        attr->type() == DT_STRING) {
      *uncompilable_reason =
          "Const op with type DT_STRING is not supported by XLA.";
      return false;
    }
  }

    for (int i = 0; i < 2; ++i) {
      tensor_proto.add_int_val(1);
    }
    NodeDef const_def;
    TF_CHECK_OK(NodeDefBuilder("const", "Const")
                    .Attr("dtype", DT_INT32)
                    .Attr("value", tensor_proto)
                    .Finalize(&const_def));
    Status s;
    Node *const_node = g->AddNode(const_def, &s);
    TF_CHECK_OK(s);

    NodeDef fn_def;