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());
}

                         AbstractContext* ctx) {
  auto CallFn = [fn_name, fn, ctx](TaggedValue args_,
                                   TaggedValue kwargs_) -> TaggedValue {
    std::cout << "Calling " << fn_name << std::endl;
    tensorflow::StatusOr<TaggedValue> v = fn.Execute(ctx, args_);
    return v.value();
  };
  return TaggedValue(CallFn);
}

// Import a module from a saved model.
//

                      dst_index);
    }
    return absl::OkStatus();
  }

  auto input_arg = mlir::cast<BlockArgument>(src);
  auto input_node_it = args_.find(input_arg);
  TF_RET_CHECK(input_node_it != args_.end())
      << "Use of BlockArgument encounted before def!";
  // For argument, there is only one result output, so the index is always 0.

                      dst_index);
    }
    return absl::OkStatus();
  }

  auto input_arg = mlir::cast<BlockArgument>(src);
  auto input_node_it = args_.find(input_arg);
  TF_RET_CHECK(input_node_it != args_.end())
      << "Use of BlockArgument encounted before def!";
  // For argument, there is only one result output, so the index is always 0.

  bool PropagateParamsAndReturnIfChanged();

  // Inserts the Quantize and Dequantize ops according to the propagation
  // result.
  void Finalize();

  SmallVector<BlockArgument, 4> GetArgs() { return args_; }

  llvm::DenseMap<std::pair<mlir::Operation*, int>, int> GetResultStates() {
    return result_states_;
  }

  DenseMap<OpWithResultIndex, QuantStateIndex> result_states_;

    src_arg_pairs->push_back({src_node, arg});
    args_.push_back(arg);
  }
  Node* dst_node = edge->dst();
  Node* dst_image = node_images.at(dst_node);
  int dst_slot = edge->dst_input();
  args_by_dst_[InputTensor(dst_node, dst_slot)] = arg_index;
  graph_->AddEdge(args_[arg_index], 0, dst_image, dst_slot);
  return absl::OkStatus();
}

          user->setOperand(operand_num, new_constant_op);
        }
      }
    }
  });
}

void QuantizationDriver::SetupAllStates() {
  for (BlockArgument arg : fn_.getArguments()) {
    args_.push_back(arg);
    Value value = arg;
    // If the argument is quantized, it should only has one user.
    if (arg.hasOneUse()) {
      Operation* user = value.use_begin().getUser();

        given:
        sourceFile << """
            #define HEADER_123 "hello.h"
            #define HEADER(X, Y) X ## Y
            #include HEADER(HEADER_, 123)
        """

        expect:
        resolve() == [header]
    }

# CHECK-SAME: _xla_compile_device_type = "GPU"
# CHECK-SAME: allow_soft_placement
# CHECK-SAME: control_outputs = ""
# CHECK-SAME: inputs = "args_0,args_1,args_2,args_3"
# CHECK-SAME: outputs = "rets_0,rets_1"
# CHECK:          %[[ISLAND_0:.*]], %[[ISLAND_0_control:.*]] = tf_executor.island wraps "tf.Const"

NODES=4

args1=()
args2=()
for i in $(seq 1 $NODES); do
	args1+=("http://localhost:$((9000 + i))/tmp/xl/1/$i ")
	args2+=("http://localhost:$((9100 + i))/tmp/xl/2/$i ")
done

for i in $(seq 1 $NODES); do
	./minio server --address "127.0.0.1:$((9000 + i))" ${args1[@]} & # | tee /tmp/minio/node.$i &
	./minio server --address "127.0.0.1:$((9100 + i))" ${args2[@]} & # | tee /tmp/minio/node.$i &
done