This transformation pass modifies the input and output types of the function
      to what are specified. The task was not just adding cast operations, but,
      instead, using tfl.quantize and tfl.dequantize ops to scale the tensors.
  }];
  let constructor = "CreateModifyIONodesPass()";
  let dependentDialects = ["TFL::TensorFlowLiteDialect"];
  let options = [
      ListOption<"io_node_types_", "test-io-types", "std::string",

func.func @relu_qint8(%arg0: tensor<1x!tf_type.qint8>) -> tensor<1x!tf_type.qint8> {
  // CHECK: %[[X:.*]] = "tf.Relu"(%arg0) : (tensor<1xi8>) -> tensor<1xi8>
  %0 = "tf.Relu"(%arg0) : (tensor<1x!tf_type.qint8>) -> tensor<1x!tf_type.qint8>
  func.return %0: tensor<1x!tf_type.qint8>
}

// -----

// CHECK-LABEL: func @if_qint8(%arg0: tensor<i1>, %arg1: tensor<1xi8>, %arg2: tensor<1xi8>) -> tensor<1xi8>

  %0 = "tf.MyAddN"(%arg0, %arg1, %arg2) {N=3:i32} : (tensor<2x3xf32>, tensor<2x3xf32>, tensor<2x3xf32>) -> tensor<2x3xf32>
  func.return %0 : tensor<2x3xf32>

// CHECK-NEXT: %[[ADD0:.*]] = "tf.RiscAdd"(%arg0, %arg1) : (tensor<2x3xf32>, tensor<2x3xf32>) -> tensor<*xf32>

// CHECK-SAME: -> (tensor<*xi32> {mhlo.sharding = "\0A\0B\0C"}, tensor<*xi1> {mhlo.sharding = "\0D\0E\0F"})
func.func @func_with_sharding_after_read_variable(%arg0: tensor<*x!tf_type.resource<tensor<32xf32>>>, %arg1: tensor<*x!tf_type.resource<tensor<32xf32>>>) -> (tensor<*xi32>, tensor<*xi1>) {
  %0 = "tf.ReadVariableOp"(%arg0) : (tensor<*x!tf_type.resource<tensor<32xf32>>>) -> tensor<32xf32>

^bb0(%arg0: tensor<1x2xf32>, %arg1: tensor<f32>, %arg2: tensor<f32>):
  %cst_0 = arith.constant dense<[2, -1]> : tensor<2xi64>
  %0 = "tf.FakeQuantWithMinMaxVars"(%arg0, %arg1, %arg2) {num_bits = 5, narrow_range = false} : (tensor<1x2xf32>, tensor<f32>, tensor<f32>) -> tensor<1x2xf32>
  %1 = "tf.Reshape"(%0, %cst_0) : (tensor<1x2xf32>, tensor<2xi64>) -> tensor<2x1xf32>

  %res = "tf.Relu6"(%biasadd) : (tensor<*xf32>) -> tensor<*xf32>
  %q_res = "quantfork.qcast"(%res) : (tensor<*xf32>) -> tensor<*x!quant.uniform<i8:f32, 0.023529411764705882:-128>>
  %dq_res = "quantfork.dcast"(%q_res) : (tensor<*x!quant.uniform<i8:f32, 0.023529411764705882:-128>>) -> tensor<*xf32>

  func.return %dq_res : tensor<*xf32>
}

^bb0(%arg0: tensor<1x2xf32>, %arg1: tensor<f32>, %arg2: tensor<f32>):
  %cst_0 = arith.constant dense<[2, -1]> : tensor<2xi64>
  %0 = "tf.FakeQuantWithMinMaxVars"(%arg0, %arg1, %arg2) {num_bits = 3, narrow_range = false} : (tensor<1x2xf32>, tensor<f32>, tensor<f32>) -> tensor<1x2xf32>
  %1 = "tf.Reshape"(%0, %cst_0) : (tensor<1x2xf32>, tensor<2xi64>) -> tensor<2x1xf32>

  %6 = "tfl.quantize"(%5) {qtype = tensor<1x112x112x32x!quant.uniform<u8:f32, 0.023528476789885875>>} : (tensor<1x112x112x32xf32>) -> tensor<1x112x112x32x!quant.uniform<u8:f32, 0.023528476789885875>>
  func.return %6 : tensor<1x112x112x32x!quant.uniform<u8:f32, 0.023528476789885875>>

func.func @decompose_fused_n(%arg0: tensor<1x2x3x4x!tf_type.string>, %arg1: tensor<f32>, %arg2: tensor<f32>) -> tensor<f32> {
  %0:2 = "tf.FusedN"(%arg0, %arg1, %arg2) {A=0:index} : (tensor<1x2x3x4x!tf_type.string>, tensor<f32>, tensor<f32>) -> (tensor<1x2x3x4x!tf_type.string>, tensor<f32>)
  func.return %0#1 : tensor<f32>

// CHECK-NEXT: %[[in0:.*]] = "tfr.cast"(%arg0) : (tensor<1x2x3x4x!tf_type.string>) -> !tfr.tensor

      %outputs_55, %control_56 = tf_executor.island wraps "tf.TensorListStack"(%outputs_53#3, %outputs_27) {_tpu_replicate = "cluster_sample_sequence", device = "", num_elements = 1024 : i64} : (tensor<!tf_type.variant>, tensor<1xi32>) -> tensor<*xf32>