%3:3 = tf_executor.Merge %2#0, %0#0 : tensor<*x!tf_type.int32ref> {device = "", N = 2, T = "tfdtype$DT_INT32"} loc("while/Merge")
    %4:2 = tf_executor.island(%3#2) wraps "tf.Const"() {device = "", dtype = "tfdtype$DT_INT32", value = dense<10> : tensor<i32>} : () -> tensor<i32> loc("while/Less/y")

    %1:2 = tf_executor.island wraps "tf.Identity"(%0#0) {T = "tfdtype$DT_FLOAT", device = ""} : (tensor<f32>) -> tensor<f32> loc("identity")

    %3:3 = tf_executor.Merge %2#0, %0#0 : tensor<*xi32> {device = "", N = 2, T = "tfdtype$DT_INT32"} loc("while/Merge")
    %4:2 = tf_executor.island(%3#2) wraps "tf.Const"() {device = "", dtype = "tfdtype$DT_INT32", value = dense<10> : tensor<i32>} : () -> tensor<i32> loc("while/Less/y")

// CHECK-SAME: (%[[ARG_0:[a-z0-9]*]]: tensor<*x!tf_type.resource>, %[[ARG_1:[a-z0-9]*]]: tensor<*x!tf_type.resource>)
func.func @replicate_resource_var_arg_shape(%arg0: tensor<*x!tf_type.resource>, %arg1: tensor<*x!tf_type.resource>) {
  %0:6 = tf_device.replicate([%arg0, %arg1] as %ri: tensor<*x!tf_type.resource>) {n = 2: i32} {
    %1 = "tf.ReadVariableOp"(%ri) {dtype = "tfdtype$DT_FLOAT"} : (tensor<*x!tf_type.resource>) -> tensor<*xf32>

      %outputs_5, %control_6 = tf_executor.island wraps "tf.TPUReplicatedInput"(%outputs_3) {N = 1 : i64, T = "tfdtype$DT_FLOAT", device = "", name = "input0"} : (tensor<0xf32>) -> tensor<0xf32>

    %0:2 = tf_executor.island wraps "tf.Const"() {dtype = "tfdtype$DT_INT32", value = dense<0> : tensor<i32>} : () -> (tensor<i32>) loc("foo")
    // CHECK: name: "foo{{_.*_1}}"
    %1:2 = tf_executor.island wraps "tf.Const"() {dtype = "tfdtype$DT_INT32", value = dense<1> : tensor<i32>} : () -> (tensor<i32>) loc("foo")
    // CHECK: name: "foo1"

  %0 = "tf.Einsum"(%arg0, %arg1) {T = "tfdtype$DT_FLOAT", equation = "ijk,j->i"}: (tensor<4x5x6xf32>, tensor<5xf32>) -> tensor<4xf32>
  func.return %0 : tensor<4xf32>
// CHECK-LABEL: einsum_no_match
// CHECK: %[[v0:.*]] = "tf.Einsum"(%arg0, %arg1) <{equation = "ijk,j->i"}> {T = "tfdtype$DT_FLOAT"} : (tensor<4x5x6xf32>, tensor<5xf32>) -> tensor<4xf32>
// CHECK: return %[[v0]]
}

        attr_map = ""
      } : (tensor<*x!tf_type.qint8>, tensor<*x!tf_type.qint8>, tensor<*xf32>, tensor<*xi32>, tensor<*xf32>, tensor<*xi32>, tensor<*xf32>, tensor<*xi32>) -> tensor<*x!tf_type.qint32>
    func.return %conv_out : tensor<*x!tf_type.qint32>
  }

  // MatMul.
  func.func private @internal_matmul_fn(%input : tensor<*x!tf_type.qint8>, %filter : tensor<*x!tf_type.qint8>,

    // The following node would be renamed to bar2
    %3:2 = tf_executor.island wraps "tf.Identity"(%2) {device = "", dtype = "tfdtype$DT_INT32"} : (tensor<10xi32>) -> tensor<10xi32> loc ("bar")

                                %weight_scale, %weight_zp) {
        Tlhs = "tfdtype$DT_FLOAT",
        Trhs = "tfdtype$DT_QINT8",
        Tout = "tfdtype$DT_FLOAT",
        rhs_quantization_axis = -1,
        rhs_quantization_min_val = -128,
        rhs_quantization_max_val = 127,
        attr_map = ""
      } : (tensor<*xf32>, tensor<*x!tf_type.qint8>, tensor<*xf32>, tensor<*xi32>) -> tensor<*xf32>