argn := len(args)
			if argn < 2 || argn > 3 {
				return types.NoSuchOverloadErr()
			}
			str, ok := args[0].Value().(string)
			if !ok {
				return types.MaybeNoSuchOverloadErr(args[0])
			}
			n := int64(-1)
			if argn == 3 {
				n, ok = args[2].Value().(int64)
				if !ok {

func.func @check_enqueue_ops_with_different_attr_disallowed(%arg0: tensor<?x2xi32>, %arg1: tensor<?x2xi32>,
  %arg2 :tensor<?x2xi32>, %arg3: tensor<?xi32>, %arg4: tensor<?xi32>, %arg5: tensor<?xi32>,
  %arg6: tensor<!tf_type.string>, %arg7: tensor<!tf_type.string>, %arg8: tensor<i1>) -> () {
  %0 = "tf.Const"() {value = dense<[]> : tensor<0xf32>} : () -> tensor<0xf32>

// CHECK-SAME:  ([[ARG0:%.+]]: tensor<i1>, [[ARG1:%.+]]: tensor<i32>, [[ARG2:%.+]]: tensor<i64>, [[ARG3:%.+]]: tensor<f32>, [[ARG4:%.+]]: tensor<f64>, [[ARG5:%.+]]: tensor<!tf_type.resource>, [[ARG6:%.+]]: tensor<!tf_type.resource>)
func.func @replace_multiple_outputs(%arg0: tensor<i1>, %arg1: tensor<i32>, %arg2: tensor<i64>, %arg3: tensor<f32>, %arg4: tensor<f64>, %arg5: tensor<!tf_type.resource>, %arg6: tensor<!tf_type.resource>) {

module {
  func.func public @main(%arg0: tensor<3x3xf32>, %arg1: tensor<3x3xf32>, %arg2: tensor<1x100x32x4xf32>,
      %arg3: tensor<1x500x4x4xf32>, %arg4: tensor<1x500x4x4xf32>, %arg5: tensor<1x1x100x500xf32>, %arg6: tensor<f32>)
      -> tensor<1x100x32x4xf32> {

    %cst = "tf.Const"() {value = dense<127> : tensor<i32>} : () -> tensor<i32>
    %cst_0 = "tf.Const"() {value = dense<-128> : tensor<i32>} : () -> tensor<i32>
    %0 = "tf.Cast"(%arg0) {Truncate = false} : (tensor<1x2240x2240x3xi8>) -> tensor<1x2240x2240x3xi32>

  func.return %4 : tensor<128x128x!quant.uniform<u8:f32, 0.2:125>>

// CHECK: "tfl.custom_tf"
// CHECK-NEXT: ^bb0(%arg4: tensor<128x128xf32>, %arg5: tensor<1xf32>, %arg6: tensor<1xf32>, %arg7: tensor<1xi32>):
// CHECK-NEXT:   "tf.LayerNorm"(%arg4, %arg5, %arg6, %arg7) {_tfl_quant_trait = "fully_quantizable", device = ""} : (tensor<128x128xf32>, tensor<1xf32>, tensor<1xf32>, tensor<1xi32>) -> tensor<128x128xf32>

  }

  // CHECK-LABEL: func @_func
  // CHECK-SAME: %[[ARG0:.*]]: tensor<?xi32>,
  // CHECK-SAME: %[[ARG1:.*]]: tensor<?xi32> {mhlo.is_same_data_across_replicas = true}
  // CHECK-SAME: %[[ARG2:.*]]: tensor<?xi32>)
  func.func @_func(%arg0: tensor<?xi32>, %arg1: tensor<?xi32>, %arg2: tensor<?xi32>) -> tensor<?xi32> {
    %0 = "tf._D"(%arg0, %arg1) : (tensor<?xi32>, tensor<?xi32>) -> tensor<?xi32>
    func.return %0 : tensor<?xi32>

    func.return
}

func.func private @__reduce_func_2(%arg0: tensor<i64> {tf._user_specified_name = "args_0"}, %arg1: tensor<32xf32> {tf._user_specified_name = "args_1"}, %arg2: tensor<!tf_type.resource<tensor<64xf32>>>, %arg3: tensor<!tf_type.resource<tensor<128xf32>>>)
  -> (tensor<i64>) attributes {tf._tf_data_function = true, tf.signature.is_stateful} {

}

# CHECK-NO-SUBTYPE: func @main(%arg0: tensor<i32>, %arg1: tensor<!tf_type.variant>, %arg2: tensor<i8>, %arg3: tensor<f64>, %arg4: tensor<!tf_type.resource>) -> (tensor<i32>, tensor<!tf_type.variant>, tensor<i8>, tensor<f64>, tensor<!tf_type.resource>)

  // CHECK-DAG: ml_program.global public @vars.Variable_1([]) : tensor<!tf_type.string>
  func.func @lowers_string_ops(%arg0: tensor<128xi32>, %arg1: tensor<128xi32>, %arg2: tensor<128x1xi32>, %arg3: tensor<128x90xi32>, %arg4: tensor<128x90xi32>, %arg5: tensor<128x90xi32>, %arg6: tensor<128x90x64xf32>, %arg7: tensor<128x90x64xf32>) -> tensor<!tf_type.string> {
    // CHECK: %[[v0:.*]] = ml_program.global_load @vars.Variable_1 : tensor<!tf_type.string>