#include "tensorflow/core/platform/strcat.h"
#include "tensorflow/core/platform/tstring.h"
#include "tensorflow/core/platform/types.h"

namespace {

// Struct that stores the status and TF_Tensor inputs to the opkernel.
// Used to delete tensor and status in its destructor upon kernel return.
struct Params {
  TF_Tensor* tags;
  TF_Tensor* values;
  TF_Status* status;
  explicit Params(TF_OpKernelContext* ctx)

}

def CreateOp: FallbackSync_Op<"createop", []> {
  let summary = "The Fallback CreateOp";

  let description = [{
    The CreateOp creates the tensorflow::OpKernel in the fallback context.

    op_key: The unique key to identify this op.
    num_args: The number of inputs.
    device: The tensorflow device. eg. "/CPU:0"

// ---------------------  Coordination service  --------------------------------
// Returns a not owning pointer to the coordination service agent, which is
// opaque to plugin. Plugin OpKernels need to use the accompanying C APIs to
// access coordination service functionalities.
TF_CAPI_EXPORT extern TF_CoordinationServiceAgent*
TF_GetCoordinationServiceAgent(TF_OpKernelContext* ctx);

// the `Cast` will be preserved. This transform is needed for the graph to be
// executed on TPU or GPU devices, which do not have `Cast` registered as a
// runtime OpKernel.
void RemoveIdentityCast(NodeDef* node_def) {
  auto attr = node_def->mutable_attr();
  if (node_def->op() == "Cast" && attr->contains("SrcT") &&
      attr->contains("DstT") &&

        self._bias = None
        if bias_shape is not None:
          self._bias = array_ops.constant(
              np.random.uniform(size=bias_shape), dtype=dtypes.float32
          )

        self._kernel = np.random.uniform(size=y_shape).astype('f4')
        self._min = (-0.8, -0.8, -0.9)
        self._max = (0.9, 0.9, 1.0)

      @def_function.function(
          input_signature=[

  auto add = Add(root.WithKernelLabel("AddWithKernelLabel"), 1.0f, 2.0f);
  TF_EXPECT_OK(root.status());
  AttrSlice attrs = add.z.op().node()->attrs();
  const auto* kernel_attr = attrs.Find("_kernel");
  ASSERT_TRUE(kernel_attr);
  TF_EXPECT_OK(AttrValueHasType(*kernel_attr, "string"));
  EXPECT_EQ(kernel_attr->s(), "AddWithKernelLabel");
}

TEST(CCOpTest, ColocateWith) {

}

def CreateOp: FallbackAsync_Op<"createop", [CoreRT_TypedAttributeTrait]> {
  let summary = "The Fallback CreateOp";

  let description = [{
    The CreateOp creates the tensorflow::OpKernel in the fallback context.

    op_key: The unique key to identify this op.
    num_args: The number of inputs.
    device: The tensorflow device. eg. "/CPU:0"

include "tensorflow/compiler/mlir/tfrt/ir/mlrt/mlrt_dialect.td"

def CreateOp: TensorflowMlrt_Op<"createop", []> {
  let summary = "The Fallback CreateOp";

  let description = [{
    The CreateOp creates the tensorflow::OpKernel in the fallback context.
  }];

  let arguments = (ins
    StrAttr:$node_def,
    I32Attr:$op_key
  );

  let assemblyFormat = "attr-dict";
}

def ConstOp: TensorflowMlrt_Op<"constop", []> {

  /// of setting the status on the scope.
  Scope ExitOnError() const;

  /// Return a new scope. All ops created with the new scope will have
  /// kernel_label as the value for their '_kernel' attribute;
  Scope WithKernelLabel(const string& kernel_label) const;

  // The following functions are for scope object consumers.

  /// Return a unique name, using default_name if an op name has not been

constexpr char kUncompilableFunctionNodeName[] = "n_c_uncompilable";
constexpr char kUncompilableFunctionTwoName[] = "UncompilableFnTwo";
constexpr char kUncompilableFunctionNodeTwoName[] = "n_d_uncompilable";

// A dummy OpKernel for testing.
class DummyCompilableOp : public XlaOpKernel {
 public:
  explicit DummyCompilableOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {}
  void Compile(XlaOpKernelContext* ctx) override {