def "can read from stream and then restart to use another stream"() {
        def input1 = encoded("string 1")
        def input2 = encoded("string 2")

        given:
        def decoder = new KryoBackedDecoder(input1)
        decoder.readString()
        decoder.restart(input2)

        expect:
        decoder.readPosition == 0
        decoder.readString() == "string 2"

        return new AbstractModelAction<T>(reference, descriptor, input1, input2) {
            @Override
            public void execute(MutableModelNode modelNode, List<ModelView<?>> inputs) {
                action.execute(modelNode,
                    Cast.<I>uncheckedCast(inputs.get(0).getInstance()),
                    Cast.<J>uncheckedCast(inputs.get(1).getInstance())
                );
            }
        };
    }

// CHECK-LABEL: @main_stream_0
// CHECK-SAME: ([[input0:%.*]]: !tf_mlrt.tensor, [[promise_b:%.*]]: !mlrt.promise)
func.func @main_stream_0(%input0: tensor<i32>, %promise_b: !mlrt.promise) {
  %const = "tf.Const"() {__op_key = 0 : i32, value = dense<1> : tensor<i32>} : () -> tensor<i32>
  // CHECK: [[a:%.*]] = tf_mlrt.executeop([[input0]],
  // CHECK-SAME: AddV2

    }

    private static String customTaskCode(String input1, String input2) {
        """
            ${customTaskImpl()}
            task customTask(type: CustomTask){
                input1 = '$input1'
                input2 = '$input2'
            }
        """
    }

    private static String customTaskImpl() {
        """

		},
		{
			name:         "Success when response contains multiple certs",
			in:           input1,
			extraCerts:   serverCerts,
			statusCode:   http.StatusOK,
			body:         validSpiffeX509BundleWithMultipleCerts,
			wantNumCerts: 2,
		},
		{
			name:        "Bundle endpoint is not trusted",
			in:          input1,
			extraCerts:  nil,
			statusCode:  http.StatusOK,
			body:        validSpiffeX509Bundle,

  EXPECT_EQ("Tensor<type: float shape: [2,3] values: [1 2 3][4 5 6]>",
            output->DebugString(100));
}

REGISTER_OP("DoNothingOp")
    .Input("input1: float")
    .Input("input2: float")
    .Attr("NumInput3: int >= 0")
    .Input("input3: NumInput3 * float")
    .Output("output1: float")
    .Attr("SomeDataTypeAttr: type");

TEST_F(DeviceKernelOpTest, TestGetKernelInfo) {

// CHECK-NEXT:    name: "min_runtime_version",
// CHECK-NEXT:    buffer: 6
// CHECK-NEXT:  } ],
// CHECK-NEXT:  signature_defs: [ {
// CHECK-NEXT:    inputs: [ {
// CHECK-NEXT:      name: "input1",
// CHECK-NEXT:      tensor_index: 1
// CHECK-NEXT:    }, {
// CHECK-NEXT:      name: "input2"
// CHECK-NEXT:    } ],
// CHECK-NEXT:    outputs: [ {
// CHECK-NEXT:      name: "end_logits",
// CHECK-NEXT:      tensor_index: 4

      (BinaryOp (TFL_ReshapeOp:$lhs $input1, (Arith_ConstantOp:$shape1 $s1)),
                (TFL_ReshapeOp:$rhs $input2, (Arith_ConstantOp:$shape2 $s2))),
      (TFL_ReshapeOp (BinaryOp $input1, $input2), $shape1),
      [(IsTailOfShape $rhs, $lhs),
       (IsTailOfShape $lhs, $rhs),
       (IsTailOfShape $input1, $input2),
       (IsTailOfShape $input2, $input1),
       (SameElementType $input1, $input2)]>;

  %10 = "tf.opK"() : () -> tensor<*xi16>
  %11 = "tf.opL"() : () -> tensor<*xi64>
  tf_device.replicate([%0, %1, %2] as %input0: tensor<*xi1>, %9 as %input1: tensor<*xi8>, %10 as %input2: tensor<*xi16>, [%3, %4, %5] as %input3: tensor<*xi32>, [%6, %7, %8] as %input4: tensor<*xf32>, %11 as %input5: tensor<*xi64>) {n = 3 : i32} {
    tf_device.return
  }
  func.return

// CHECK:      %[[OP_A:[a-z0-9]*]] = "tf.opA"

// CHECK-NEXT:    name: "min_runtime_version",
// CHECK-NEXT:    buffer: 8
// CHECK-NEXT:  } ],
// CHECK-NEXT:  signature_defs: [ {
// CHECK-NEXT:    inputs: [ {
// CHECK-NEXT:      name: "input1",
// CHECK-NEXT:      tensor_index: 1
// CHECK-NEXT:    }, {
// CHECK-NEXT:      name: "input2"
// CHECK-NEXT:    } ],
// CHECK-NEXT:    outputs: [ {
// CHECK-NEXT:      name: "end_logits",
// CHECK-NEXT:      tensor_index: 5