>, tensor<i8>, tensor<f64>, tensor<!tf_type.resource>)

# CHECK-SUBTYPE: func @main(%arg0: tensor<10xi32>, %arg1: tensor<!tf_type.variant<tensor<10xf32>>>, %arg2: tensor<10xi8>, %arg3: tensor<10xf64>, %arg4: tensor<!tf_type.resource<tensor<10xi32>>>) -> (tensor<10xi32>, tensor<!tf_type.variant<tensor<10xf32>>>, tensor<10xi8>, tensor<10xf64>, tensor<!tf_type.resource<tensor<10xi32>>>)...

  # checking.
  #
  # Note: CHECK-DAG doesn't work with CHECK-SAME/CHECK-NEXT.

  # Check index paths for results.
  #
  # CHECK:      func {{@[a-zA-Z_0-9]+}}() -> (
  # CHECK-SAME:   tensor<1xf32> {tf_saved_model.index_path = []})
  # CHECK-SAME: attributes {{.*}} tf_saved_model.exported_names = ["f0000_single_return"]
  @tf.function(input_signature=[])
  def f0000_single_return(self):

// CHECK-NEXT: tfl.gather
// CHECK-NEXT: tfl.dequantize
}

// CHECK-LABEL: dequantize_pushdown_gather_with_reduction
func.func @dequantize_pushdown_gather_with_reduction(%arg0: tensor<2xi32>) -> tensor<2x2xf32> {
  %w = "tfl.pseudo_qconst"() {qtype = tensor<12x2x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>, value = dense<127> : tensor<12x2xi8>} : () -> tensor<12x2x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>

// CHECK: %[[UDQ:.*]] = stablehlo.uniform_dequantize %[[BITCAST_CONVERT_1]] : (tensor<1x!quant.uniform<i8:f32, 1.000000e+00:3>>) -> tensor<1xf32>
// CHECK: return %[[UDQ]] : tensor<1xf32>
module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 268 : i32}} {
    func.func @main(%arg0 : tensor<?xf32>) -> tensor<?xf32> {

  tfg.func @test_func_name0(%test_func_name0: tensor<*x!tf_type.resource> {tf._output_shapes = #tf_type.shape<*>, tf._resource_arg_unique_id = 0 : i64, tfg.name = "test_func_name0"},...

  func.return %0 : tensor<!tf_type.variant<tensor<2xi32>>>

module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 268 : i32}} {
  func.func @main() -> () {
     %s0 = "tf.Const"() {value = dense<[4, 1]> : tensor<2xi32>} : () -> tensor<2xi32>
    func.return
  }

  # Check index paths for arguments.
  # The outer layer of the index path indexes into the arguments.
  #
  # CHECK:      func {{@[a-zA-Z_0-9]+}}(
  # CHECK-SAME:   %arg0: tensor<1xf32> {tf._user_specified_name = "x", tf_saved_model.index_path = [0]},
  # CHECK-SAME:   %arg1: tensor<2xf32> {tf._user_specified_name = "y", tf_saved_model.index_path = [1]})

    // CHECK-NEXT: %[[UDQ:.*]] = "tf.UniformDequantize"(%[[UQ]], %[[CST_0]], %[[CST_1]]) <{quantization_axis = -1 : i64, quantization_max_val = 127 : i64, quantization_min_val = -128 : i64}> : (tensor<1x!tf_type.qint8>, tensor<f32>, tensor<i32>) -> tensor<1xf32>
    // CHECK-NEXT: return %[[UDQ]] : tensor<1xf32>

}

func.func @"__inference_Dataset_map_<lambda>_80"(%arg0: tensor<*xi32>) -> tensor<*xi32> {
  %0 = "tf.Const"() {value = dense<2> : tensor<i32>} : () -> tensor<i32>
  %1 = "tf.Mul"(%arg0, %0) {device = ""} : (tensor<*xi32>, tensor<i32>) -> tensor<*xi32>
  %2 = "tf.Identity"(%1) {device = ""} : (tensor<*xi32>) -> tensor<*xi32>
  func.return %2 : tensor<*xi32>
}