%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 { size: 10 }", "tfshape$dim { size: -1 } dim { size: 9 } dim { size: 10 }", "tfshape$dim...

func.func @testReadFileOpNotFolded() -> (tensor<!tf_type.string>) {
  %0 = "tf.Const"() { value = dense<"filepath"> : tensor<!tf_type.string> } : () -> tensor<!tf_type.string>
  // CHECK: %[[X:.*]] = "tf.ReadFile"
  %1 = "tf.ReadFile"(%0) : (tensor<!tf_type.string>) -> tensor<!tf_type.string>
  // CHECK: return %[[X]]
  func.return %1: tensor<!tf_type.string>

      # Escapes template $-based substitutions for attributes containing $.
      # $$ is replaced with a single $.
      func_template = string.Template(
          func_match.group(2).replace('tfdtype$DT_', 'tfdtype$$DT_'))
    except Exception as e:  # pylint: disable=broad-except
      raise ValueError('The function template is in wrong format') from e

    replacement_text = ''
    for value_dict in value_list:

// RUN: tf-mlir-translate -mlir-to-graphdef %s -o - | FileCheck %s

func.func @main() {
  tf_executor.graph {
    %outputs, %control = tf_executor.island wraps "tf.Const"() {device = "", dtype = "tfdtype$DT_INT32", name = "Constant", value = dense<0> : tensor<i32>} : () -> tensor<i32>
    %outputs_0, %control_1 = tf_executor.island wraps "tf.LegacyCall"(%outputs) {_tpu_replicate = "cluster", device = "", f = @foo0} : (tensor<i32>) -> tensor<i32>
    tf_executor.fetch
  }

func.func @main() {
  tf_executor.graph {
  // CHECK:      node {
  // CHECK-NEXT:   name: "Const"
  // CHECK-NEXT:   op: "Const"
    %0:2 = tf_executor.island wraps "tf.Const"() {device = "", dtype = "tfdtype$DT_FLOAT", value = dense<2.500000e-01> : tensor<f32>} : () -> tensor<f32> loc("Const")

  // CHECK:      node {
  // CHECK-NEXT:   name: "tf.PartitionedCall"
  // CHECK-NEXT:   op: "PartitionedCall"
  // CHECK:        func {

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-SAME:  (%[[VAR_HANDLE:.*]]: tensor<*x!tf_type.resource>, %[[MS_HANDLE:.*]]: tensor<*x!tf_type.resource>, %[[MOM_HANDLE:.*]]: tensor<*x!tf_type.resource>,
// CHECK-SAME:   %[[LR:.*]]: tensor<f32>, %[[RHO:.*]]: tensor<f32>, %[[MOMENTUM:.*]]: tensor<f32>, %[[EPSILON:.*]]: tensor<f32>, %[[GRAD:.*]]: tensor<f32>)

  %fq = "tf.FakeQuantWithMinMaxVars"(%in, %mini, %maxi) {num_bits = 3, narrow_range = false} : (tensor<3x3x3x16xf32>, tensor<f32>, tensor<f32>) -> tensor<3x3x3x16xf32>
  %rst = "tf.Conv2D"(%arg, %fq) {T = "tfdtype$DT_FLOAT", data_format = "NHWC", dilations = [1, 2, 3, 1], padding = "SAME", strides = [1, 4, 5, 1]} : (tensor<256x32x32x3xf32>, tensor<3x3x3x16xf32>) -> tensor<256x8x7x16xf32>
  func.return %rst : tensor<256x8x7x16xf32>

namespace tensorflow {
namespace mangling_util {
namespace {

using ::mlir::tfg::mangling_util::PrintShortTextProto;

const char kAttributePrefix[] = "tf.";
const char kDataTypePrefix[] = "tfdtype$";
const char kTensorShapePrefix[] = "tfshape$";
const char kTensorPrefix[] = "tftensor$";

}  // namespace

string MangleAttributeName(absl::string_view str) {
  return absl::StrCat(kAttributePrefix, str);
}

  %fq = "tf.FakeQuantWithMinMaxVars"(%in, %mini, %maxi) {num_bits = 5, narrow_range = false} : (tensor<3x3x3x16xf32>, tensor<f32>, tensor<f32>) -> tensor<3x3x3x16xf32>
  %rst = "tf.Conv2D"(%arg, %fq) {T = "tfdtype$DT_FLOAT", data_format = "NHWC", dilations = [1, 2, 3, 1], padding = "SAME", strides = [1, 4, 5, 1]} : (tensor<256x32x32x3xf32>, tensor<3x3x3x16xf32>) -> tensor<256x8x7x16xf32>
  func.return %rst : tensor<256x8x7x16xf32>