FunctionDef make_ref_float = FunctionDefHelper::Define(
      "RefFloatFn", {}, {"r:float"}, {},
      {{{"var"},
        "VariableV2",
        {},
        {{"dtype", DT_FLOAT}, {"shape", TensorShape({})}}},
       {{"r"}, "Identity", {"var"}, {{"T", DT_FLOAT}}}});
  *fdef_lib->add_function() = make_ref_float;
}

void AddRegularFunctionFunctionDef(FunctionDefLibrary* fdef_lib) {

    size_t idx = type_and_idx.index();
    auto result_ty = mlir::cast<mlir::RankedTensorType>(type_and_idx.value());

    // If the result type isn't static, then the owner of the result may be a
    // cast op from a more specific bounded type to an unbounded dynamic type.
    // Use the bounded type to get the buffer size.
    mlir::RankedTensorType buffer_ty = result_ty;
    if (!buffer_ty.hasStaticShape()) {

  TF_Status* status = TF_NewStatus();
  // Create the variable handle.
  TFE_Op* op = TFE_NewOp(ctx, "VarHandleOp", status);
  if (TF_GetCode(status) != TF_OK) return nullptr;
  TFE_OpSetAttrType(op, "dtype", TF_FLOAT);
  TFE_OpSetAttrShape(op, "shape", {}, 0, status);
  TFE_OpSetAttrString(op, "container", "localhost", 0);
  TFE_OpSetAttrString(op, "shared_name", "", 0);
  if (!device_name.empty()) {

  }
  strings::StrAppend(&ret, "}");
  return ret;
}

string PrintTensor(const TensorProto& tensor_proto) {
  Tensor t(tensor_proto.dtype());
  CHECK(t.FromProto(tensor_proto));
  const int64_t num_elts = t.NumElements();
  switch (t.dtype()) {
    case DT_FLOAT:
      return PrintArray(num_elts, t.flat<float>().data());
    case DT_DOUBLE:
      return PrintArray(num_elts, t.flat<double>().data());

  const tensorflow::Tensor& arg_tensor = cc_ctx->input(index);
  absl::Status cc_status;
  if (arg_tensor.dtype() != tensorflow::DT_RESOURCE) {
    cc_status = absl::InvalidArgumentError(
        absl::StrCat("Trying to obtain resource handle from Input[", index,
                     "], which is not type DT_RESOURCE."));
    status->status = cc_status;
    return nullptr;
  }
  const tensorflow::ResourceHandle& handle =

  }

  // Derive the output types. The result type is derived by using the
  // attributes attched to the result type of the signature. The attribute
  // value should be either in the attribute argument list or the derived
  // attribute from the input tensors. All the result type
  // are unranked, and shape inference should be applied afterwards.
  SmallVector<Type, 4> output_types;

           op_name == "TruncatedNormal" || op_name == "Multinomial";
  }

  bool OpProducesOrConsumesVariant(const Node& node) const {
    auto is_variant = [](DataType dtype) { return dtype == DT_VARIANT; };
    return absl::c_any_of(node.input_types(), is_variant) ||
           absl::c_any_of(node.output_types(), is_variant);
  }

  bool HasXLAKernel(const Node& node,

  virtual AbstractTensorInterface* CreateBoolScalar(bool value) = 0;

  // Tensor creation functions
  virtual AbstractTensorInterface* CreateTensor(
      DataType dtype, absl::Span<const int64_t> dim_sizes) = 0;

  typedef void (*MemoryReleaser)(void* data, size_t len, void* arg);

  // Create a tensor instance from the given data buffer and description.

  // pushes and pops at the back.
  std::stack<AccumulatorCallState> call_state_;
};

// Template instantiations here

inline bool IsDtypeTrainable(DataType dtype) {
  switch (dtype) {
    case DT_HALF:
    case DT_BFLOAT16:
    case DT_FLOAT:
    case DT_DOUBLE:
    case DT_COMPLEX64:
    case DT_COMPLEX128:
    case DT_RESOURCE:
    case DT_VARIANT:

  }

  tensorflow::AbstractTensorInterface* t =
      tensorflow::unwrap(ctx)->CreateTensor(
          static_cast<tensorflow::DataType>(dtype), dimvec);

  if (t == nullptr) {
    status->status =
        tensorflow::errors::InvalidArgument("Unsupported dtype: ", dtype);
    return nullptr;
  }

  return new TF_Tensor{t};
}

TFE_TensorHandle* TFE_NewTensorHandleFromTensor(TFE_Context* ctx, TF_Tensor* t,