func.return %4 : tensor<!tf_type.variant>
  }
  // CHECK-LABEL: __inference_Dataset_flat_map_lambda_190
  func.func private @__inference_Dataset_flat_map_lambda_190(%arg0: tensor<i64> {tf._user_specified_name = "args_0"}) -> tensor<!tf_type.variant> attributes {tf._original_func_name = "__inference_Dataset_flat_map_lambda_19", tf._tf_data_function = true, tf.signature.is_stateful} {

    func.return %4, %6 : tensor<!tf_type.variant>, tensor<!tf_type.variant>
  }
  // CHECK-LABEL: @funcB_renamed
  func.func private @funcB_renamed(%arg0: tensor<i64> {tf._user_specified_name = "args_0"}) -> tensor<!tf_type.variant> attributes {tf._original_func_name = "funcB", tf._tf_data_function = true, tf.signature.is_stateful} {
    %0 = "tf.Const"() {device = "/device:CPU:0", value = dense<0> : tensor<i64>} : () -> tensor<i64>

# CHECK: "tf.AssignVariableOp"(%[[ARG_0]], %[[RAND_STD_NORMAL]]){{.*}}: (tensor<!tf_type.resource<tensor<1x3xf32>>>, tensor<1x3xf32>) -> ()
# CHECK: return

# The function for the signature "key".
# CHECK: func {{@[a-zA-Z_0-9]+}}(
# CHECK-SAME: %[[ARG_1:.*]]: tensor<3x1xf32> {tf_saved_model.index_path = ["x"]}
# CHECK-SAME: %[[ARG_2:.*]]: tensor<!tf_type.resource<tensor<1x3xf32>>> {tf_saved_model.bound_input = @y}

}

# Verify main graph was converted to a function and args/rets/control rets are
# mapped correctly.

# CHECK-LABEL: func @main
# CHECK-SAME:  (%{{.*}}: tensor<*xf32>, %[[ARG_1:.*]]: tensor<*x!tf_type.resource<tensor<f32>>>, %[[ARG_2:.*]]: tensor<*x!tf_type.resource<tensor<f32>>>)
# CHECK-SAME:  control_outputs = "var1_add,var2_add"
# CHECK-SAME:  inputs = "arg0,arg1,arg2"
# CHECK-SAME:  outputs = "ret"

  l.append(Integer(3));
  EXPECT_EQ(l.Get<Integer>(0)->get(), 3);
  EXPECT_EQ(l.size(), 1);
}

TaggedValue AddIntegers(TaggedValue args_, TaggedValue kwargs_) {
  auto& args = args_.tuple();
  // auto& kwargs = kwargs_.dict();
  return TaggedValue(args[0].i64() + args[1].i64());
}

TEST(ObjectTest, TestCast) {
  Integer i(3);
  auto result = Cast<String>(i);
  ASSERT_TRUE(!result.ok());
}

Daniel Martí <******@****.***> 1610820211 +0000

// CHECK-SAME: (%[[ARG_0:[a-z0-9]*]]: tensor<*xf32>, %[[ARG_1:[a-z0-9]*]]: tensor<*xi32>)
func.func @return_multiple_operands(%arg_0: tensor<*xf32>, %arg_1: tensor<*xi32>) {
  %result:2 = "tf_device.launch"() ({
// CHECK:   tf_device.return %[[ARG_0]], %[[ARG_1]] : tensor<*xf32>, tensor<*xi32>
    tf_device.return %arg_0, %arg_1 : tensor<*xf32>, tensor<*xi32>
  }) {device = "device"} : () -> (tensor<*xf32>, tensor<?xi32>)
  func.return

// run arg1 arg2

// Copyright 2009 The Go Authors. All rights reserved.
// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.

// Test os.Args.

package main

import "os"

func main() {
	if len(os.Args) != 3 {
		panic("argc")
	}
	if os.Args[1] != "arg1" {
		panic("arg1")
	}
	if os.Args[2] != "arg2" {
		panic("arg2")
	}

zirain <******@****.***> 1684861711 +0800

type ReaderArgs struct {
	unmarshaled bool
}

// IsEmpty - returns whether reader args is empty or not.
func (args *ReaderArgs) IsEmpty() bool {
	return !args.unmarshaled
}

// UnmarshalXML - decodes XML data.
func (args *ReaderArgs) UnmarshalXML(d *xml.Decoder, start xml.StartElement) error {
	// Make subtype to avoid recursive UnmarshalXML().
	type subReaderArgs ReaderArgs