%4 = "tfl.reshape"(%3, %cst) : (tensor<1x112x112x32x!quant.uniform<i8:f32, 0.023528476789885875>>, tensor<2xi32>) -> tensor<1x401408x!quant.uniform<i8:f32, 0.023528476789885875>>
  %5 = "tfl.softmax"(%4) <{beta = 1.000000e+00 : f32}> : (tensor<1x401408x!quant.uniform<i8:f32, 0.023528476789885875>>) -> tensor<1x401408x!quant.uniform<i8:f32, 3.906250e-03>>

 "tf_saved_model.global_tensor"() {sym_name = "v2", type = tensor<f32>, value = dense<1.0> : tensor<f32> } : () -> ()

  func.func @f(%arg0: tensor<!tf_type.resource<tensor<f32>>> {tf_saved_model.bound_input = @v})
  attributes {tf_saved_model.exported_names = ["f"]} {
    %val = "tf.ReadVariableOp"(%arg0) : (tensor<!tf_type.resource<tensor<f32>>>) -> tensor<f32>
    func.return

// CHECK-LABEL: not_reset_input
func.func @not_reset_input(%arg0: tensor<f32>) -> (tensor<!quant.uniform<i16:f32, 1.0>>) {
  %0 = "tfl.quantize"(%arg0) {qtype = tensor<!quant.uniform<i16:f32, 1.0>>} : (tensor<f32>) -> tensor<!quant.uniform<i16:f32, 1.0>>
  func.return %0: tensor<!quant.uniform<i16:f32, 1.0>>

// CHECK-NEXT: %[[q:.*]] = "tfl.quantize"(%arg0) <{qtype = tensor<!quant.uniform<i16:f32, 1.000000e+00>>}>
// CHECK-NEXT: return %[[q]]
}

    %3 = "tf.NotEqual"(%1, %2) : (tensor<f32>, tensor<f32>) -> tensor<i1>
    "tf.Yield"(%3) : (tensor<i1>) -> ()
  },  {
  ^bb0(%arg3: tensor<f32>, %arg4: tensor<*xf32>):
    %cst = "tf.Const"() {value = dense<1.0> : tensor<f32>} : () -> tensor<f32>
    %1 = "tf.Sub"(%arg3, %cst) : (tensor<f32>, tensor<f32>) -> tensor<f32>
    "tf.Yield"(%1, %arg4) : (tensor<f32>, tensor<*xf32>) -> ()

func.func @unknown_resource_op_while_body(%arg0: !tf_res, %arg1: !tf_res, %arg2: tensor<f32>, %arg3: tensor<f32>) -> (!tf_res, !tf_res, tensor<f32>, tensor<f32>) {
  %graph:4 = tf_executor.graph {

func.func @testAbs(tensor<? x f32>) -> tensor<? x f32> {
^bb0(%arg0: tensor<? x f32>):
  // CHECK: "tfl.abs"(%arg0)
  %0 = "tfl.abs"(%arg0): (tensor<? x f32>) -> tensor<? x f32>
  func.return %0 : tensor<? x f32>
}

// CHECK-LABEL: testAddN
func.func @testAddN(tensor<? x f32>, tensor<? x f32>, tensor<? x f32>) -> tensor<? x f32> {
^bb0(%arg0: tensor<? x f32>, %arg1: tensor<? x f32>, %arg2: tensor<? x f32>):

  %2 = "tf.ReadVariableOp"(%arg2) : (!tf_res_f32) -> tensor<f32>
  %execute_output:3 = "tf_device.parallel_execute"() ({
    %3:3 = "tf_device.cluster_func"(%0, %1, %2) {_xla_compile_device_type = "TPU", _replication_info = "tpu", func = @device_func_5} : (tensor<f32>, tensor<f32>, tensor<f32>) -> (tensor<f32>, tensor<f32>, tensor<f32>)
    tf_device.return %3#0, %3#1, %3#2 : tensor<f32>, tensor<f32>, tensor<f32>

    %cst_0 = "tf.Const"() {value = dense<0.0156862754> : tensor<f32>} : () -> tensor<f32>
    %cst_1 = "tf.Const"() {value = dense<-5.000000e-01> : tensor<f32>} : () -> tensor<f32>
    %cst_2 = "tf.Const"() {value = dense<-1> : tensor<i32>} : () -> tensor<i32>
    %cst_3 = "tf.Const"() {value = dense<0.0274509806> : tensor<f32>} : () -> tensor<f32>

    %1 = stablehlo.uniform_quantize %0 : (tensor<1x3x!quant.uniform<i32:f32:1, {3.000000e-05,3.000000e-05,3.000000e-05}>>) -> tensor<1x3x!quant.uniform<i8:f32, 1.000000e-03:-3>>

    %6 = "quantfork.dcast"(%5) : (tensor<1x3x!quant.uniform<i8:f32, 0.13170163023705575:-1>>) -> tensor<1x3xf32>
    %7 = "quantfork.qcast"(%arg2) {volatile} : (tensor<f32>) -> tensor<!quant.uniform<i8:f32, 0.13170163023705575:-1>>
    %8 = "quantfork.dcast"(%7) : (tensor<!quant.uniform<i8:f32, 0.13170163023705575:-1>>) -> tensor<f32>