%1:2 = "tfl.while"(%cst, %0) ({
    ^bb0(%arg1: tensor<*xi32>, %arg2: tensor<*x!tf_type.resource<tensor<*xi32>>>):
      %2 = "tf.ReadVariableOp"(%arg2) {dtype = i32} : (tensor<*x!tf_type.resource<tensor<*xi32>>>) -> tensor<*xi32>
      %3 = "tfl.greater"(%arg1, %2) : (tensor<*xi32>, tensor<*xi32>) -> tensor<i1>
      "tfl.yield"(%3) : (tensor<i1>) -> ()
  },  {
    ^bb0(%arg3: tensor<*xi32>, %arg4: tensor<i32>):

// CHECK-NEXT:  buffer: 10
// CHECK-NEXT:  } ]
// CHECK-NEXT:  signature_defs: [ ]
// CHECK-NEXT:}

^bb0(%arg0: tensor<1x384xf32>, %arg1: tensor<1x96xf32>, %arg2: tensor<384x480xf32>, %arg3: tensor<384xf32>, %arg4: tensor<1x96xf32>):

  #
  # CHECK:      func {{@[a-zA-Z_0-9]+}}(
  # CHECK-SAME:   %arg0: tensor<1xf32> {tf._user_specified_name = "d", tf_saved_model.index_path = [0, "a"]},
  # CHECK-SAME:   %arg1: tensor<2xf32> {tf._user_specified_name = "d", tf_saved_model.index_path = [0, "b"]},
  # CHECK-SAME:   %arg2: tensor<3xf32> {tf._user_specified_name = "d", tf_saved_model.index_path = [0, "c"]},

// CHECK-LABEL: func @quantized_types
func.func @quantized_types(%arg0: tensor<!tf_type.resource<tensor<1x3x!tf_type.quint8>>>,
                      %arg1: tensor<!tf_type.resource<tensor<1x3x!tf_type.quint16>>>,
                      %arg2: tensor<!tf_type.resource<tensor<1x3x!tf_type.qint8>>>,
                      %arg3: tensor<!tf_type.resource<tensor<1x3x!tf_type.qint16>>>,
                      %arg4: tensor<!tf_type.resource<tensor<1x3x!tf_type.qint32>>>) {

^bb0(%arg0: tensor<8xf32>, %arg3: tensor<f32>, %arg4: tensor<f32>):
  %1 = "tf.FakeQuantWithMinMaxVars"(%arg0, %arg3, %arg4) {num_bits = 5, narrow_range = false} : (tensor<8xf32>, tensor<f32>, tensor<f32>) -> tensor<8xf32>
  func.return %1 : tensor<8xf32>

// CHECK: %0 = "tf.FakeQuantWithMinMaxVars"(%arg0, %arg1, %arg2)
// CHECK: return %0 : tensor<8xf32>
}

# CHECK-SAME: attributes {allow_soft_placement = false, tf.entry_function = {control_outputs = "", inputs = "arg0,arg1,arg2", outputs = "ret0,ret1,ret2"}}...

// CHECK:       tf.OpC
func.func @result_used_in_cond(%arg0: tensor<*xf32>, %arg1: tensor<*xf32>) -> tensor<*xf32> {
  %0:2 = "tf.WhileRegion"(%arg0, %arg1) ({
  ^bb0(%arg2: tensor<*xf32>, %arg3: tensor<*xf32>):
    %1 = "tf.OpA"(%arg2) {is_stateless = true} : (tensor<*xf32>) -> tensor<i1>
    "tf.Yield"(%1) : (tensor<i1>) -> ()
  }, {
  ^bb0(%arg2: tensor<*xf32>, %arg3: tensor<*xf32>):

^bb0(%arg0: tensor<8xf32>, %arg3: tensor<f32>, %arg4: tensor<f32>):
  %1 = "tf.FakeQuantWithMinMaxVars"(%arg0, %arg3, %arg4) {num_bits = 3, narrow_range = false} : (tensor<8xf32>, tensor<f32>, tensor<f32>) -> tensor<8xf32>
  func.return %1 : tensor<8xf32>

// CHECK: %0 = "tf.FakeQuantWithMinMaxVars"(%arg0, %arg1, %arg2)
// CHECK: return %0 : tensor<8xf32>
}

func.func @op_with_region_mixed_tfl_shlo_tfl(%arg0: tensor<7x5xf32>, %arg1 : tensor<5xf32>) -> tensor<5xf32> {
  %0 = "stablehlo.reduce"(%arg0, %arg1) ({
  ^bb0(%arg2: tensor<5xf32>, %arg3: tensor<5xf32>):
    // CHECK:      %1 = "tfl.abs"(%arg2) {fused_activation_function = "NONE"} : (tensor<5xf32>) -> tensor<5xf32>
    // CHECK-NEXT: %2 = "vhlo.add_v1"(%1, %arg2) : (tensor<5xf32>, tensor<5xf32>) -> tensor<5xf32>

// CHECK-NOT: tf
func.func @strips_attributes(%arg0: tensor<32x28x28x1xf32> {tf._user_specified_name = "x"},
                             %arg1: tensor<3x3x1x5xf32> {tf._user_specified_name = "w1"},
                             %arg2: tensor<5xf32> {tf._user_specified_name = "b1"},
                             %arg3: tensor<3920x10xf32> {tf._user_specified_name = "w2"},