// CHECK-NEXT: [[o_chain_0:%.*]], [[o1:%.*]] = tfrt_fallback_async.executeop.seq([[in_chain]]) key(1) cost({{.*}}) device("/job:localhost/replica:0/task:0/device:CPU:0") "tf.ReadVariableOp"([[o]]) {dtype = f32} : 1
// CHECK-NEXT: [[out_ch:%.*]] = tfrt.merge.chains [[o_chain]], [[o_chain_0]]
// CHECK-NEXT: tfrt.return [[out_ch]], [[o1]] : !tfrt.chain, !tfrt_fallback.tf_tensor

                                                void* data, size_t len,
                                                TF_Status* status) {
  auto dtype = static_cast<tensorflow::DataType>(data_type);
  DCHECK(tensorflow::DataTypeCanUseMemcpy(dtype));

  tensorflow::Tensor tensor(dtype, tensorflow::TensorShape({}));
  std::memcpy(tensorflow::TensorCApi::Buffer(tensor)->data(), data, len);

  status->status = absl::OkStatus();

	if done {
		return
	}

	code := StatusOK

	// If Content-Type isn't set, use the file's extension to find it, but
	// if the Content-Type is unset explicitly, do not sniff the type.
	ctypes, haveType := w.Header()["Content-Type"]
	var ctype string
	if !haveType {
		ctype = mime.TypeByExtension(filepath.Ext(name))
		if ctype == "" {
			// read a chunk to decide between utf-8 text and binary

                        AttrValueMap* values) {
  AttrValue value;
  auto& type_list = *value.mutable_list();
  for (auto type : types) {
    DataType dtype;
    TF_RETURN_IF_ERROR(ConvertScalarTypeToDataType(type, &dtype));
    type_list.add_type(dtype);
  }

  auto result = values->insert({string(name), value});
  assert(result.second && "cannot have multiple attributes with the same name");
  (void)result;

// depending on indir. The top parameter is whether we are at the top
// level, where Go pointers are allowed. Go pointers to pinned objects are
// allowed as long as they don't reference other unpinned pointers.
func cgoCheckArg(t *_type, p unsafe.Pointer, indir, top bool, msg string) {
	if !t.Pointers() || p == nil {
		// If the type has no pointers there is nothing to do.
		return
	}

      shape_determination_fns_.layout_preference_fn(
          device_tensor->shape(), device_tensor->dtype(), std::nullopt);
  Status status = [&]() -> Status {
    TF_ASSIGN_OR_RETURN(xla::Shape shape,
                        shape_determination_fns_.shape_representation_fn(
                            device_tensor->shape(), device_tensor->dtype(),
                            /*fast_mem=*/false, layout_preference));

  TF_DeleteExecutionContext(ctx);
  return wrap(func);
}

TF_AbstractTensor* TF_AddFunctionParameter(TF_ExecutionContext* func,
                                           TF_DataType dtype, TF_Shape shape,
                                           TF_Status* s) {
  DCHECK_GE(shape.num_dims, -1);
  TracingTensorHandle* t;
  TracingContext* tracing_ctx = dyn_cast<TracingContext>(unwrap(func));

  %1 = "tf.Const"() {device = "/device:CPU:0", value = dense<[1.0]> : tensor<1xbf16>} : () -> tensor<4xbf16>
  // CHECK: corert.executeop({{.*}}) "tf.Const"() {dtype = ui64, value = dense<[1, 2, 3, 4]> : tensor<4xui64>} : 1
  %2 = "tf.Const"() {device = "/device:GPU:0", value = dense<[1, 2, 3, 4]> : tensor<4xui64>} : () -> tensor<4xui64>
  func.return %0 : tensor<4xui64>
}

}

// tryRegAssignArg tries to register-assign a value of type t.
// If this type is nested in an aggregate type, then offset is the
// offset of this type within its parent type.
// Assumes t.size <= goarch.PtrSize and t.size != 0.
//
// Returns whether the assignment succeeded.
func (p *abiDesc) tryRegAssignArg(t *_type, offset uintptr) bool {
	switch k := t.Kind_ & abi.KindMask; k {

    Location loc = graph_func.getLoc();
    FunctionType ftype = graph_func.getFunctionType();

    func::FuncOp func = rewriter.create<func::FuncOp>(
        graph_func.getLoc(),
        graph_func->getAttrOfType<StringAttr>(SymbolTable::getSymbolAttrName())
            .getValue(),
        ftype);

    func->setAttrs(graph_func->getAttrs());

    llvm::SmallVector<Type> arg_types;
    llvm::SmallVector<Type> res_types;