%0 = "tf.Const"() {device = "/device:CPU:0", value = dense<1> : tensor<i32>} : () -> tensor<i32>
    %1 = "tf.AddV2"(%arg0, %0) {device = "/device:CPU:0"} : (tensor<i32>, tensor<i32>) -> tensor<i32>
    %2 = "tf.Div"(%arg1, %0) {device = "/device:CPU:0"} : (tensor<i32>, tensor<i32>) -> tensor<i32>
    func.return %1, %2 : tensor<i32>, tensor<i32>
  }

  "tf.AssignVariableOp"(%arg1, %0#1) : (tensor<*x!tf_type.resource<tensor<i32>>>, tensor<i32>) -> ()
  func.return
}

// CHECK-LABEL: func @multiple_write_func
// CHECK-SAME: ({{%.*}}: tensor<i32>) -> (tensor<i32>, tensor<i32>)
func.func @multiple_write_func(%arg0: tensor<i32>) -> (tensor<i32>, tensor<i32>) {
  func.return %arg0, %arg0 : tensor<i32>, tensor<i32>
}

func.func @listReserveScalarShapeI32(%arg0: tensor<i32>, %arg1: tensor<i32>) -> tensor<!tf_type.variant<tensor<*xi32>>> {
  %0 = "tf.TensorListReserve"(%arg0, %arg1) : (tensor<i32>, tensor<i32>) -> tensor<!tf_type.variant<tensor<*xi32>>>
  // CHECK: %0 = "tfl.custom"(%arg0, %arg1) <{custom_code = "TensorListReserve", custom_option = #tfl<const_bytes : "0x02">}> : (tensor<i32>, tensor<i32>) -> tensor<!tf_type.variant<tensor<*xi32>>>

  func.return %0#2, %0#3, %0#4 : tensor<i32>, tensor<256x256xf32>, tensor<?x256x256xf32>
}

module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 268 : i32}} {
    // CHECK-LABEL: func.func @main
    // CHECK-DAG: %[[CST_0:.*]] = "tf.Const"() <{value = dense<1.000000e+00> : tensor<f32>}> : () -> tensor<f32>
    // CHECK-DAG: %[[CST_1:.*]] = "tf.Const"() <{value = dense<3> : tensor<i32>}> : () -> tensor<i32>

  // CHECK: "tfl.average_pool_2d"(%{{.*}}) <{filter_height = 3 : i32, filter_width = 6 : i32, fused_activation_function = "NONE", padding = "VALID", stride_h = 3 : i32, stride_w = 1 : i32}> : (tensor<1x6x6x16xf32>) -> tensor<1x1x1x16xf32>
  %0 = "tfl.average_pool_2d"(%arg0) {filter_height = 3 : i32, filter_width = 6 : i32, fused_activation_function = "NONE", padding = "VALID", stride_h = 3 : i32, stride_w = 1 : i32} : (tensor<1x6x6x16xf32>) -> tensor<1x1x1x16xf32> loc("avgpool")

  %input_zps = "tf.Const"() { value = dense<4> : tensor<i32> } : () -> tensor<i32>
  %bias_scales = "tf.Const"() { value = dense<2.0> : tensor<f32> } : () -> tensor<f32>
  %bias_zps = "tf.Const"() { value = dense<4> : tensor<i32> } : () -> tensor<i32>
  %output_scales = "tf.Const"() { value = dense<2.0> : tensor<f32> } : () -> tensor<f32>
  %output_zps = "tf.Const"() { value = dense<4> : tensor<i32> } : () -> tensor<i32>

// CHECK-LABEL: import_stats_skip
func.func @import_stats_skip(%arg0: tensor<4xf32>, %cst: tensor<i32>) -> (tensor<2xf32>,tensor<2xf32>) {
  %0:2 = "tfl.split"(%cst, %arg0) {num_splits = 2 : i32} : (tensor<i32>, tensor<4xf32>) -> (tensor<2xf32>, tensor<2xf32>)
    loc(fused["skip1", "skip2.cc":10:8, callsite("op" at "skip3.cc":10:8)])
  func.return %0#0, %0#1 : tensor<2xf32>, tensor<2xf32>

// CKECK-SAME: <{calibration_method = 5 : i32, id = "skipping_id", num_bins = 32 : i32, max_percentile = 0.000000e+00 : f32, min_percentile = 0.000000e+00 : f32}> : (tensor<1x3x4x3xf32>) -> (tensor<1x3x4x3xf32>, tensor<f32>, tensor<f32>, tensor<512xi64>)
// CHECK: %[[CUSTOM_AGGREGATOR_1:.*]], %[[MIN_1:.*]], %[[MAX_1:.*]], %[[HISTOGRAM_1:.*]] = "tf.CustomAggregator"

// CHECK:           [[VAL_1:%.*]] = "tfl.average_pool_2d"([[VAL_0]]) <{filter_height = 48 : i32, filter_width = 48 : i32, fused_activation_function = "NONE", padding = "VALID", stride_h = 1 : i32, stride_w = 1 : i32}> : (tensor<1x48x48x512xf32>) -> tensor<1x1x1x512xf32>
// CHECK:           return [[VAL_1]] : tensor<1x1x1x512xf32>
// CHECK:         }

// -----