>, tensor<i8>, tensor<f64>, tensor<!tf_type.resource>)

# CHECK-SUBTYPE: func @main(%arg0: tensor<10xi32>, %arg1: tensor<!tf_type.variant<tensor<10xf32>>>, %arg2: tensor<10xi8>, %arg3: tensor<10xf64>, %arg4: tensor<!tf_type.resource<tensor<10xi32>>>) -> (tensor<10xi32>, tensor<!tf_type.variant<tensor<10xf32>>>, tensor<10xi8>, tensor<10xf64>, tensor<!tf_type.resource<tensor<10xi32>>>)...

  func.return %0 : tensor<256x32x32x16xf32>
}

func.func @testConv2DGPU(tensor<256x32x32x3xf32>, tensor<16x3x3x3xf32>, tensor<16xf32>) -> tensor<256x32x32x16xf32> {
^bb0(%arg0: tensor<256x32x32x3xf32>, %arg1: tensor<16x3x3x3xf32>, %arg2: tensor<16xf32>):
  // CHECK: tac.cost = 0x4C300000

    %0:2 = tf_executor.island wraps "tf.Const"() {dtype = "tfdtype$DT_INT32", value = dense<0> : tensor<10xi32>} : () -> tensor<10xi32>
    %1:2 = tf_executor.island wraps "tf.Const"() {dtype = "tfdtype$DT_INT32", value = dense<0> : tensor<i32>} : () -> tensor<i32>
    %2:2 = tf_executor.island wraps "tf.PlaceholderWithDefault"(%1#0) {type = i32} : (tensor<i32>) -> tensor<*xi32> loc("unranked")

// CHECK:            shape {
// CHECK-NEXT:         unknown_rank: true


func.func @main() {
  tf_executor.graph {
    %0:4 = tf_executor.island wraps "tf.InfeedDequeueTuple"() : () -> (tensor<3xi32>, tensor<4x?xf32>, tensor<*xi16>)
    tf_executor.fetch
  }
  func.return

# CHECK-LABEL: func @main
# CHECK-SAME:  ([[VAL_0:%.*]]: tensor<1x8x8x2xi32>) -> (tensor<1x8x8x2xi32>, tensor<i32>, tensor<10xi32>)
# CHECK-SAME:  control_outputs = ""
# CHECK-SAME:  inputs = "unranked"
# CHECK-SAME:  outputs = "unranked,static,static_10"
# CHECK-DAG:         [[VAL_2:%.*]] = arith.constant dense<0> : tensor<10xi32>
# CHECK-DAG:         [[VAL_1:%.*]] = arith.constant dense<0> : tensor<i32>

  // CHECK-NEXT: %[[NEW_SIZE:.*]] = "tf.AddV2"(%[[ZERO]], %[[CONST1]]) : (tensor<1xi32>, tensor<1xi32>) -> tensor<1xi32>
  %push = "tf.TensorListPushBack"(%id, %elem) : (tensor<!tf_type.variant<tensor<f32>>>, tensor<f32>) -> tensor<!tf_type.variant<tensor<f32>>>
  // CHECK-NEXT: %[[COPY:.*]] = "tf.Identity"(%[[UPDATE]])
  // CHECK-NEXT: %[[CONST1_1:.*]] = "tf.Const"() <{value = dense<1> : tensor<1xi32>}> : () -> tensor<1xi32>

  // CHECK-DAG: %[[X_SHAPE:.+]] = shape.shape_of %[[X]] : tensor<3x4x256x6xf32> -> tensor<4xindex>
  // CHECK-DAG: %[[EPS:.+]] = mhlo.constant dense<1.000000e+00> : tensor<256xf32>
  // CHECK-DAG: %[[MEAN:.+]] = "tf.Mean"(%arg0, %[[CST_AXIS]]) <{keep_dims = false}> : (tensor<3x4x256x6xf32>, tensor<3xi32>) -> tensor<256xf32>

# NONE-SAME:  control_outputs = ""
# NONE-SAME:  inputs = "input0,input1"
# NONE-SAME:  outputs = "Add"
# NONE:           %[[add:.*]], %[[add_control:.*]] = tf_executor.island wraps "tf.Add"(%[[ARG_0]], %[[ARG_1]])
# NONE:           fetch %[[add]]

# UNKNOWN-LABEL: func @main

  func.return %0 : tensor<2xi32>
}

// -----

// CHECK-LABEL: listFromTensor
func.func @listFromTensor(%tensor: tensor<3xi32>, %shape : tensor<?xi32>) -> tensor<!tf_type.variant<tensor<i32>>> {
  %0 = "tf.TensorListFromTensor"(%tensor, %shape) : (tensor<3xi32>, tensor<?xi32>) -> tensor<!tf_type.variant<tensor<i32>>>

func.func @main(
    %arg0: tensor<10x10xf32>, %scale: tensor<10xf32>, %zp: tensor<10xi32>
  ) -> tensor<10x10xi8> {
  %0 = "tf.UniformQuantize"(%arg0, %scale, %zp) {
    quantization_axis = 1 : i64,
    quantization_min_val = -128 : i64,
    quantization_max_val = 127 : i64
  } : (tensor<10x10xf32>, tensor<10xf32>, tensor<10xi32>) -> tensor<10x10x!tf_type.qint8>