%1 = "quantfork.stats"(%arg0) {layerStats = dense<[-1.0, 1.0]> : tensor<2xf32>} : (tensor<1x1x3xf32>) -> tensor<1x1x3xf32>
  %2 = "tfl.pseudo_const"() {value = dense<[-1, 3]> : tensor<2xi32>} : () -> tensor<2xi32>
  %3 = "tfl.reshape"(%1, %2) : (tensor<1x1x3xf32>, tensor<2xi32>) -> tensor<1x3xf32>
  %4 = "quantfork.stats"(%3) {layerStats = dense<[-1.0, 1.0]> : tensor<2xf32>} : (tensor<1x3xf32>) -> tensor<1x3xf32>
  func.return %4 : tensor<1x3xf32>

    // CHECK-NOT:  tf.Acos
    %0 = "tf.Acos"(%arg0) : (tensor<2xf32>) -> tensor<2xf32>
    func.return %0 : tensor<2xf32>
  }


  // CHECK-LABEL: unknown_op
  func.func @unknown_op(%arg0: tensor<2xf32>) -> tensor<2xf32> {
    // CHECK: tf.CustomTestOp
    %0 = "tf.CustomTestOp"(%arg0) : (tensor<2xf32>) -> tensor<2xf32>

    func.return %0 : tensor<2xf32>
  }

  // CHECK-LABEL: add_v2

// CHECK-LABEL: floor_mod
func.func @floor_mod(%arg0: tensor<5xf32>, %arg1: tensor<5xf32>) -> tensor<5xf32> {
  %0 = "tf.FloorMod"(%arg0, %arg1) : (tensor<5xf32>, tensor<5xf32>) -> tensor<5xf32>
  func.return %0 : tensor<5xf32>
// CHECK: %[[CUSTOM_0:.*]] = "tfl.custom"(%arg0, %arg1) <{custom_code = "FlexFloorMod", custom_option = #tfl<const_bytes : "{{.*}}">}> : (tensor<5xf32>, tensor<5xf32>) -> tensor<5xf32>
// CHECK: return %[[CUSTOM_0]] : tensor<5xf32>
}

// CHECK-NEXT:  %[[quant:.*]] = "tfl.quantize"(%[[split]]#0) <{qtype = tensor<2x!quant.uniform<u8:f32, 1.000000e+00>>}> {volatile} : (tensor<2xf32>) -> tensor<2x!quant.uniform<u8:f32, 1.000000e+00>>
// CHECK-NEXT:  return %[[quant]] : tensor<2x!quant.uniform<u8:f32, 1.000000e+00>>

  func.return %1#0 : tensor<1x3x2x2xf32>
}
// CHECK: func.func @main(%[[ARG:.+]]: tensor<1x3x4x3xf32>) -> tensor<1x3x2x2xf32> {
// CHECK-DAG: %[[CONST_0:.*]] = stablehlo.constant dense<[{{.*}}]> : tensor<2xf32>

  } : (tensor<2x2x!tf_type.qint8>, tensor<2xf32>, tensor<2xi32>, tensor<2xf32>, tensor<2xi32>) -> tensor<2x2x!tf_type.qint8>
  %2 = "tf.UniformDequantize"(%1, %scales_1, %zps_1) {
    quantization_axis = 0 : i64, quantization_min_val = -128 : i64, quantization_max_val = 127 : i64
  } : (tensor<2x2x!tf_type.qint8>, tensor<2xf32>, tensor<2xi32>) -> tensor<2x2xf32>
  func.return %2 : tensor<2x2xf32>
}

    layerStats = dense<[-1.28e-5, 1.27e-5]> : tensor<2xf32>
  } : (tensor<1x2xf32>) -> tensor<1x2xf32>
  %w = arith.constant dense<[[0.0, 1.0], [1.0, 2.0]]> : tensor<2x2xf32>
  %w_q = "quantfork.stats"(%w) {
    layerStats = dense<[0.0, 2.0]> : tensor<2xf32>
  } : (tensor<2x2xf32>) -> tensor<2x2xf32>
  %b = arith.constant dense<[0.0, 2.1473647e3]> : tensor<2xf32>
  %fc = "tfl.fully_connected"(%0, %w_q, %b) {

  %1:2 = "tfl.split"(%cst, %0) {num_splits = 2 : i32} : (tensor<i32>, tensor<4xf32>) -> (tensor<2xf32>, tensor<2xf32>)
  %2 = "tfl.quantize"(%1#0) {qtype = tensor<2x!quant.uniform<u8:f32, 1.0>>} : (tensor<2xf32>) -> tensor<2x!quant.uniform<u8:f32, 1.0>>
  %3 = "tfl.quantize"(%1#1) {qtype = tensor<2x!quant.uniform<u8:f32, 1.0>>} : (tensor<2xf32>) -> tensor<2x!quant.uniform<u8:f32, 1.0>>

  // CHECK: %0 = mhlo.constant dense<[0, 1]> : tensor<2xi32>
  // CHECK-NEXT: %1 = mhlo.constant dense<[3, 7]> : tensor<2xi32>
  // CHECK-NEXT: %2 = mhlo.constant dense<[1, 3]> : tensor<2xi32>
  // CHECK-NEXT: %3 = "tf.StridedSlice"(%arg0, %0, %1, %2) : (tensor<4x8xf32>, tensor<2xi32>, tensor<2xi32>, tensor<2xi32>) -> tensor<3x2xf32>
  %begin = "tf.Const"() {value = dense<[0, 1]> : tensor<2xi32>} : () -> (tensor<2xi32>)

// -----

// CHECK-LABEL: standalone_avgpool_test
func.func @standalone_avgpool_test(%arg0: tensor<*xf32>) -> tensor<*xf32> {
  %cst = arith.constant dense<[-1, 144]> : tensor<2xi32>
  %0 = "quantfork.qcast"(%arg0) : (tensor<*xf32>) -> tensor<*x!quant.uniform<i8:f32, 5.000000e-02:-10>>
  %1 = "quantfork.dcast"(%0) : (tensor<*x!quant.uniform<i8:f32, 5.000000e-02:-10>>) -> tensor<*xf32>