// WHOLE-PASSES-DAG:  %[[rv2:.*]] = "tfl.read_variable"({{.*}}) : (tensor<*x!tf_type.resource<tensor<1x2x3x!quant.uniform<u8:f32, {{.*}}>>>>) -> tensor<1x2x3x!quant.uniform<u8:f32, {{.*}}>>

  let arguments = (
    ins TFL_TensorOf<[F32, QI8, QI16]>:$input,

    // Weights
    TFL_TensorOfOrNone<[F32, QI8]>:$input_to_input_weights,
    TFL_TensorOf<[F32, QI8]>:$input_to_forget_weights,
    TFL_TensorOf<[F32, QI8]>:$input_to_cell_weights,
    TFL_TensorOf<[F32, QI8]>:$input_to_output_weights,

    // Recurrent weights
    TFL_TensorOfOrNone<[F32, QI8]>:$recurrent_to_input_weights,

  func.return %6 : tensor<1x112x112x32x!quant.uniform<u8:f32, 0.023528476789885875>>
}

// DEBUG-LABEL: QuantizeSplit
func.func @QuantizeSplit(%arg: tensor<4x!quant.uniform<u8:f32, 1.0>>, %cst: tensor<i32>) -> (tensor<2x!quant.uniform<u8:f32, 1.0>>,tensor<2x!quant.uniform<u8:f32, 1.0>>) {
  %0 = "tfl.dequantize"(%arg) : (tensor<4x!quant.uniform<u8:f32, 1.0>>) -> tensor<4xf32>

      %3 = "tf.StringToNumber"(%arg1) {out_type = f32} : (tensor<!tf_type.string>) -> tensor<f32>
      "tf.Yield"(%3) : (tensor<f32>) -> ()
     },  {
      %4 = "tf.Const"() {value = dense<1.0> : tensor<f32>} : () -> tensor<f32>
      "tf.Yield"(%4) : (tensor<f32>) -> ()
    }) {is_stateless = true} : (tensor<i1>) -> (tensor<f32>)
    %5 = "tf.Identity"(%2) : (tensor<f32>) -> tensor<f32>
    tf_device.return %5 : tensor<f32>

  %0 = "tfl.pseudo_qconst"() { qtype = tensor<3x3x!quant.uniform<i32:f32:0, {1.0, 0.5:1, 0.25:1}>>, value = dense<1> : tensor<3x3xi32>} : () -> tensor<3x3x!quant.uniform<i32:f32:0, {1.0, 0.5:1, 0.25:1}>>
  func.return %0 : tensor<3x3x!quant.uniform<i32:f32:0, {1.0, 0.5:1, 0.25:1}>>
}

func.func @qu8() -> tensor<3x!quant.uniform<u8<1:255>:f32, 1.0>> {
  // CHECK-LABEL: @qu8

// CHECK: %[[CUSTOM_AGGREGATOR_1:.*]], %[[MIN_1:.*]], %[[MAX_1:.*]], %[[HISTOGRAM_1:.*]] = "tf.CustomAggregator"

         none, none, none,
         tensor<20x!quant.uniform<i8:f32, 0.006:-34>>, tensor<20x!quant.uniform<i8:f32, 0.006:-34>>, tensor<20x!quant.uniform<i8:f32, 0.006:-34>>, tensor<20x!quant.uniform<i8:f32, 0.006:-34>>,
         none, none,
         tensor<1x20x!quant.uniform<i8:f32, 0.006:-34>>, tensor<1x20x!quant.uniform<i16:f32, 0.006:-34>>,

      f = @identity} : (tensor<!tf_type.variant<tensor<f32>>>) -> tensor<!tf_type.variant<tensor<f32>>>
  %2 = "tf.OptionalGetValue"(%1) : (tensor<!tf_type.variant<tensor<f32>>>) -> tensor<f32>
  %3 = "tf.OptionalNone"() : () -> tensor<!tf_type.variant<tensor<f32>>>
  return %2, %3 : tensor<f32>, tensor<!tf_type.variant<tensor<f32>>>
}

func.func private @identity(%arg0: tensor<!tf_type.variant<tensor<f32>>>) -> tensor<!tf_type.variant<tensor<f32>>> {

^bb0(%arg0: tensor<1x56x56x24x!quant.uniform<u8:f32, 0.27583434161017922:119>>, %arg1: tensor<1x56x56x24x!quant.uniform<u8:f32, 0.40149296779258581:136>>):

  %0:4 = "tf.CustomAggregator"(%arg0) {min = -0.1 : f32, max = 0.2 : f32, id = "0", calibration_method = 1 : i32, num_bins = 0 : i32, max_percentile = 0.000000e+00 : f32, min_percentile = 0.000000e+00 : f32} : (tensor<8x8x8x8xf32>) -> (tensor<8x8x8x8xf32>, tensor<f32>, tensor<f32>, tensor<*xi64>)