// NOFOLD: %[[TRANSPOSE:[0-9]*]] = "tf.Transpose"(%arg0, %[[CST]]) : (tensor<?x224x224x3xf32>, tensor<4xi32>) -> tensor<?x3x224x224xf32>

  // Pad input with new paddings.
  // CHECK: %[[PAD:[0-9]*]] = "tf.Pad"(%arg0, %[[PADDINGS]])
  // CHECK-SAME: (tensor<?x224x224x3xf32>, tensor<4x2xi32>) -> tensor<?x230x230x3xf32>
  // NOFOLD: %[[PAD:[0-9]*]] = "tf.Pad"(%[[TRANSPOSE]], %[[PADDING]])

// RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s -o - | flatbuffer_to_string - | FileCheck %s

func.func @main(tensor<1x224x224x3xf32>) -> tensor<1x112x112x32xf32> {
^bb0(%arg0: tensor<1x224x224x3xf32>):
  // CHECK:      {
  // CHECK-NEXT:  version: 3,
  // CHECK-NEXT:  operator_codes: [ {
  // CHECK-NEXT:    deprecated_builtin_code: 6,
  // CHECK-NEXT:    version: 1,
  // CHECK-NEXT:    builtin_code: DEQUANTIZE
  // CHECK-NEXT:  }, {

// CHECK-LABEL: main
func.func @main(%arg0: tensor<1x224x224x3xf32>) -> tensor<1x401408xf32> {
// CHECK:   %{{.*}} = "tfl.quantize"(%{{.*}}) <{qtype = tensor<1x224x224x3x!quant.uniform<u8:f32, 7.812500e-03:128>>}> : (tensor<1x224x224x3xf32>) -> tensor<1x224x224x3x!quant.uniform<u8:f32, 7.812500e-03:128>>

// RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s -o - | flatbuffer_to_string - | FileCheck %s

func.func @main(tensor<1x224x224x3xf32>) -> tensor<1x112x112x32xf32> {
^bb0(%arg0: tensor<1x224x224x3xf32>):
  // CHECK:      {
  // CHECK-NEXT:  version: 3,
  // CHECK-NEXT:  operator_codes: [ {
  // CHECK-NEXT:    deprecated_builtin_code: 6,
  // CHECK-NEXT:    version: 1
  // CHECK-NEXT:    builtin_code: DEQUANTIZE
  // CHECK-NEXT:  }, {

    %0 = "tfl.dequantize"(%arg0) : (tensor<1x224x224x3x!quant.uniform<u8:f32, 1.0>>) -> tensor<1x224x224x3xf32>
    %1 = "tfl.dequantize"(%arg1) : (tensor<32x3x3x3x!quant.uniform<u8<1:255>:f32, 1.0>>) -> tensor<32x3x3x3xf32>
    %2 = "tfl.dequantize"(%arg2) : (tensor<32x!quant.uniform<i32:f32, 1.0>>) -> tensor<32xf32>

//
module {
   func @while_body {
     %input = "tf.IteratorGetNext"(...) {device = "/CPU:0"}:
              -> tensor<2x224x224x3xf32>
     %device_launch = "tf_device.launch_func"(%input,...) {func = @_func,...)
     return ...
   }
   func @_func(%input: tensor<2x224x224x3xf32>,
               %filter: tensor<7x7x3x64xf32>) {
     %6 = "tf.Conv2D"(%input, %filter)  {strides = [1, 2, 2, 1]}: