// RUN: tf-tfrt-opt -tf-to-tfrt %s | FileCheck %s --dump-input=fail

// CHECK-LABEL: func @string_tensor
func.func @string_tensor() -> (tensor<0x!tf_type.string>, tensor<7x!tf_type.string>) {
  // CHECK: {shape = [0], value = []}
  %0 = "tf.Const"() {value = dense<[]> : tensor<0x!tf_type.string>} : () -> tensor<0x!tf_type.string>

    %0 = "tf.VarHandleOp"() <{container = "", shared_name = "Variable"}> : () -> tensor<!tf_type.resource<tensor<!tf_type.string>>>
    "tf.AssignVariableOp"(%0, %cst) <{validate_shape = false}> : (tensor<!tf_type.resource<tensor<!tf_type.string>>>, tensor<!tf_type.string>) -> ()

}

func.func @string() -> tensor<2x2x!tf_type.string> {
  // CHECK-LABEL: @string
  // CHECK: value = dense<{{\[\["1", "12"\], \["123", "1234"\]\]}}> : tensor<2x2x!tf_type.string>
  %0 = "tfl.pseudo_const"() { value = dense<[["1", "12"], ["123", "1234"]]> : tensor<2x2x!tf_type.string> } : () -> tensor<2x2x!tf_type.string>
  func.return %0 : tensor<2x2x!tf_type.string>
}

func.func @string_norank() -> tensor<!tf_type.string> {

func.func private @batched_function(%arg0: tensor<1x3xf32> {tf._user_specified_name = "0"}, %arg1: tensor<*x!tf_type.resource>) -> tensor<1x3xf32> attributes {tf._input_shapes = [#tf_type.shape<1x3>, #tf_type.shape<*>], tf.signature.is_stateful} {
  %0 = "tf.ReadVariableOp"(%arg1) {device = "/device:CPU:0"} : (tensor<*x!tf_type.resource>) -> tensor<1x3xf32>

// CHECK-LABEL: func @write_only_resource
// CHECK-SAME: ([[ARG0:%.*]]: tensor<i32>, [[ARG1:%.*]]: tensor<f32>, [[ARG2:%.*]]: tensor<*x!tf_type.resource<tensor<i32>>>)
func.func @write_only_resource(%arg0: tensor<i32>, %arg1: tensor<f32>, %arg2: tensor<*x!tf_type.resource<tensor<i32>>>) {
  // CHECK-NEXT: [[READ:%.*]] = "tf.ReadVariableOp"([[ARG2]])

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>

  // CHECK: %[[CUSTOM_AGGREGATOR_2:.*]], {{.*}}, {{.*}}, {{.*}} = "tf.CustomAggregator"(%[[XLA_CALL_MODULE:.*]])

}

func.func @string() -> tensor<2x2x!tf_type.string> {
  // CHECK-LABEL: @string
  // CHECK: value = dense<{{\[\["1", "12"\], \["123", "1234"\]\]}}> : tensor<2x2x!tf_type.string>
  %0 = "tfl.pseudo_const"() { value = dense<[["1", "12"], ["123", "1234"]]> : tensor<2x2x!tf_type.string> } : () -> tensor<2x2x!tf_type.string>
  func.return %0 : tensor<2x2x!tf_type.string>
}

func.func @string_norank() -> tensor<!tf_type.string> {

TensorFlowDialect::TensorFlowDialect(MLIRContext *context)
    : Dialect(/*name=*/"tf", context, TypeID::get<TensorFlowDialect>()) {
  context->getOrLoadDialect<::mlir::tf_type::TFTypeDialect>();
  addOperations<
#define GET_OP_LIST
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_all_ops.cc.inc"
      >();
  addOperations<
#define GET_OP_LIST

  func.func @__forward_call_369(%arg0: tensor<16x224x224x3xf32> {tf._user_specified_name = "inputs"}, %arg1: tensor<*x!tf_type.resource>, %arg2: tensor<*x!tf_type.resource>, %arg3: tensor<*x!tf_type.resource>, %arg4: tensor<*x!tf_type.resource>) -> (tensor<?x?xf32>, tensor<*xf32>, tensor<?x16384xf32>, tensor<16x112x112x?xf32>, tensor<16x224x224x3xf32>, tensor<*xf32>) attributes {tf.entry_function = {control_outputs = "", inputs...