%result:2 = "tf.ParseExampleV2"(%serialized, %names, %empty_str_vector, %dense_keys, %empty_str_vector, %dense_default_0, %dense_default_1) {dense_shapes = [#tf_type.shape<>, #tf_type.shape<>], num_sparse = 0 : i64, resultSegmentSizes = array<i32: 0, 0, 0, 2, 0, 0>} : (tensor<32x!tf_type.string>, tensor<32x!tf_type.string>, tensor<0x!tf_type.string>, tensor<2x!tf_type.string>, tensor<0x!tf_type.string>, tensor<?xf32>, tensor<?xf32>)...

  %1:5 = "tf.PartitionedCall"(%arg0, %arg1, %arg2, %arg3, %arg4, %arg5) {Tin = ["tfdtype$DT_FLOAT", "tfdtype$DT_FLOAT", "tfdtype$DT_FLOAT", "tfdtype$DT_FLOAT", "tfdtype$DT_FLOAT", "tfdtype$DT_FLOAT"], Tout = ["tfdtype$DT_FLOAT", "tfdtype$DT_FLOAT", "tfdtype$DT_FLOAT", "tfdtype$DT_FLOAT", "tfdtype$DT_FLOAT"], _output_shapes = ["tfshape$dim { size: 9 } dim {...

func.func @packStringWithFlex(%arg0: tensor<2x!tf_type.string>, %arg1: tensor<2x!tf_type.string>) -> tensor<2x2x!tf_type.string> {
  %0 = "tf.Pack"(%arg0, %arg1) : (tensor<2x!tf_type.string>, tensor<2x!tf_type.string>) -> tensor<2x2x!tf_type.string>
  func.return %0 : tensor<2x2x!tf_type.string>

// CHECK-LABEL: packStringWithFlex
// CHECK: "tf.Pack"(%arg0, %arg1) : (tensor<2x!tf_type.string>, tensor<2x!tf_type.string>) -> tensor<2x2x!tf_type.string>
}

    // CHECK: Cast
    // CHECK-SAME: (tensor<!tf_type.variant<tensor<2xf32>>>) -> tensor<!tf_type.variant<tensor<2xf32>>>
    %0 = "tf.Cast"(%arg0) {Truncate = false} : (tensor<!tf_type.variant<tensor<2xf32>>>) -> tensor<!tf_type.variant>
    %1 = "tf.Identity"(%0) : (tensor<!tf_type.variant>) -> tensor<!tf_type.variant>

        // expected-remark@above {{ID: 0}}
        // expected-remark@above {{Successors: {2}}}
        "tf._InternalTestNonResourceValueSideEffects_"(%arg0) : (tensor<!tf_type.string>) -> ()
        // expected-remark@above {{ID: 1}}

func.func @testNoAddToAddV2ForStringType(%arg0: tensor<8x16x!tf_type.string>, %arg1: tensor<8x16x!tf_type.string>) -> tensor<8x16x!tf_type.string> {
  %0 = "tf.Add"(%arg0, %arg1) : (tensor<8x16x!tf_type.string>, tensor<8x16x!tf_type.string>) -> tensor<8x16x!tf_type.string>
  func.return %0: tensor<8x16x!tf_type.string>

  // CHECK: %0 = "tf.Add"(%arg0, %arg1) : (tensor<8x16x!tf_type.string>, tensor<8x16x!tf_type.string>) -> tensor<8x16x!tf_type.string>

    ```mlir
    func.func @single_state_single_dataset_type_no_arguments(%arg0: tensor<!tf_type.variant>, %arg1: tensor<i64>) {
      %0 = "tf.AnonymousIteratorV3"() {output_shapes = [#tf_type.shape<32>], output_types = [f32]} : () -> tensor<!tf_type.resource>
      "tf.MakeIterator"(%arg0, %0) : (tensor<!tf_type.variant>, tensor<!tf_type.resource>) -> ()
      %cst = "tf.Const"() {value = dense<true> : tensor<i1>} : () -> tensor<i1>

        "tf.WriteSummary"(%writer, %step, %wtensor, %tag, %wmetadata) {_xla_outside_compilation = "auto"} : (tensor<*x!tf_type.resource>, tensor<i64>, tensor<f32>, tensor<!tf_type.string>, tensor<!tf_type.string>) -> ()
        "tf.Yield"() : () -> ()
      }, {

    // CHECK:      metadata
    // CHECK:      dtype: DT_RESOURCE
    // CHECK-SAME: kind: VARIABLE

    func.return %0: tensor<*x!tf_type.resource>
  }
  func.func @_func(%arg0: tensor<*x!tf_type.resource>) -> tensor<*x!tf_type.resource> {
    func.return %arg0 : tensor<*x!tf_type.resource>
  }
}

// -----

func.func @testGatherUnsupported2DStringInput(%arg0 : tensor<?x?x!tf_type.string>, %arg1 : tensor<?xi32>) -> tensor<*x!tf_type.string> {
  // expected-error @+1 {{'tfl.gather' op expect 1d input when the given type is string, got 'tensor<?x?x!tf_type.string>'}}
  %0 = "tfl.gather"(%arg0, %arg1) {axis = 1 : i32}: (tensor<?x?x!tf_type.string>,tensor<?xi32>) -> tensor<*x!tf_type.string>
  func.return %0 : tensor<*x!tf_type.string>
}

// -----