module {
  func.func @same_scale_test(%arg0: tensor<*xf32>) -> tensor<*xf32> {
    %cst = arith.constant dense<[-1, 144]> : tensor<2xi32>
    %cst_1 = arith.constant dense<1.0> : tensor<144x10xf32>
    %cst_2 = arith.constant dense<0.1> : tensor<10xf32>
    %0 = "quantfork.qcast"(%arg0) : (tensor<*xf32>) -> tensor<*x!quant.uniform<i8:f32, 0.05:-10>>

  // CHECK-LABEL: func @parallel_execute_with_tiled_input
  // CHECK-SAME: (%[[ARG_0:[a-z0-9]*]]: tensor<128x10xf32>, %[[ARG_1:[a-z0-9]*]]: tensor<128x10xf32>, %[[ARG_2:[a-z0-9]*]]: tensor<*xi32>, %[[ARG_3:[a-z0-9]*]]: tensor<*xi32>)
  func.func @parallel_execute_with_tiled_input(%arg0: tensor<128x10xf32>, %arg1: tensor<128x10xf32>, %arg2: tensor<*xi32>, %arg3: tensor<*xi32>) -> (tensor<*xi32>, tensor<*xi1>) {
    // CHECK: tf_device.replicate

  // CHECK-SAME: -> tensor<!tf_type.variant<tensor<16x1xf32>>>
  func.func @while_variant(%arg0: tensor<!tf_type.variant<tensor<16x1xf32>>>) -> tensor<!tf_type.variant> {
    // CHECK: tf.While
    // CHECK-SAME: -> tensor<!tf_type.variant<tensor<16x1xf32>>>
    %0 = "tf.While"(%arg0) {cond = @variant_cond_func, body = @variant_body_func, is_stateless = true} : (tensor<!tf_type.variant<tensor<16x1xf32>>>) -> tensor<!tf_type.variant>

  // time constant.
  %0 = "tf.ConcatV2"(%arg0, %arg0, %arg0, %arg0, %axis) : (tensor<16x1xf32>, tensor<16x1xf32>, tensor<16x1xf32>, tensor<16x1xf32>, tensor<i32>) -> tensor<16x4xf32>

  // Just to introduce an extra use for %cst.
  %1 = "tf.AddV2"(%axis, %axis) {device = ""} : (tensor<i32>, tensor<i32>) -> tensor<i32>

  return %0 : tensor<16x4xf32>
}
// Check that the constant is cloned with same value.

    %5 = stablehlo.constant dense<2.000000e+02> : tensor<1x1xf32>  // Output inverse scale.
    %6 = stablehlo.constant dense<2> : tensor<1x1xi8>  // Output zero point.
    %7 = call @uniform_quantize_0(%arg0, %0, %1) : (tensor<1x2xf32>, tensor<1x1xf32>, tensor<1x1xi8>) -> tensor<1x2xi8>
    %8 = stablehlo.convert %7 : (tensor<1x2xi8>) -> tensor<1x2xf32>

  %elem_2 = "tf._SecondOp"() : () -> tensor<16x1xf32>
  %tl_set_item2 = "tf.TensorListSetItem"(%tl, %one, %elem_2) : (tensor<!tf_type.variant<tensor<?x1xf32>>>, tensor<i32>, tensor<16x1xf32>) -> tensor<!tf_type.variant<tensor<?x1xf32>>>
  func.return
}

// CHECK: use_two_sep_ops_empty_tensor_list

}

// -----

// CHECK-LABEL: listPushBack
func.func @listPushBack(%arg0: tensor<!tf_type.variant<tensor<?x1xf32>>>, %arg1: tensor<16x1xf32>) -> tensor<!tf_type.variant<tensor<?x1xf32>>>  {
  %0 = "tf.TensorListPushBack"(%arg0, %arg1) : (tensor<!tf_type.variant<tensor<?x1xf32>>>, tensor<16x1xf32>) -> tensor<!tf_type.variant<tensor<?x1xf32>>>

// CHECK-LABEL: @ShardingAttr
func.func @ShardingAttr(%arg0: tensor<128x10xf32> {mhlo.sharding = "\08\03\1A\02\01\02\22\02\00\01"}, %arg1: tensor<10x1024xf32> {mhlo.sharding = "\08\01\1A\01\01\22\01\00"}, %arg2: tensor<128x1024xf32> {mhlo.sharding = ""}) -> (tensor<128x10xf32>, tensor<10x1024xf32>, tensor<128x1024xf32>) {

module attributes {tf.versions = {producer = 179 : i32}} {
  func.func @main(%arg0: tensor<128x10xf32> {mhlo.sharding = "\08\03\1A\02\01\02\22\02\00\01"}, %arg1: tensor<10x1024xf32> {mhlo.sharding = "\08\01\1A\01\01\22\01\00"}, %arg2: tensor<128x1024xf32> {mhlo.sharding = ""}) {
    func.return
  }
}

  %0 = "tfl.fully_connected"(%arg0, %arg1, %arg2) {fused_activation_function = "NONE", keep_num_dims = false, weights_format = "DEFAULT"} : (tensor<2x2x10xf32>, tensor<40x40xf32>, none) -> tensor<1x40xf32>
  func.return %0 : tensor<1x40xf32>
}

// -----

func.func @testFullyConnectedWith3DFilter(%arg0: tensor<1x37xf32>, %arg1: tensor<40x2x37xf32>, %arg2: tensor<40xf32>) -> tensor<1x40xf32> {