}

// -----

func.func @pack_CPU(%arg0: tensor<100xf32>, %arg1: tensor<100xf32>) -> tensor<2x100xf32> attributes {tac.device = "CPU", tac.interface_name = "func_2"} {
  // CHECK: tac.cost = 1.000000e+02
  %0 = "tfl.pack"(%arg0, %arg1) {axis = 0 : i32, tac.device = "CPU", values_count = 2 : i32} : (tensor<100xf32>, tensor<100xf32>) -> tensor<2x100xf32>
  func.return %0 : tensor<2x100xf32>
}

func.func @no_pushdown_multiple_outputs(%arg0: tensor<1000x2x!quant.uniform<i8:f32, 7.812500e-03>>) -> tensor<1000xf32> {
  %0 = "tfl.dequantize"(%arg0) : (tensor<1000x2x!quant.uniform<i8:f32, 7.812500e-03>>) -> tensor<1000x2xf32>
  %1:2 = "tfl.unpack"(%0) {axis = 1 : i32, num = 2 : i32} : (tensor<1000x2xf32>) -> (tensor<1000xf32>, tensor<1000xf32>)
  func.return %1#0 : tensor<1000xf32>

// CHECK-NEXT: tfl.dequantize
// CHECK-NEXT: tfl.unpack
}

func.func @main(%arg0: tensor<10xi32>, %arg1: tensor<10xi32>) -> tensor<10xi32>
attributes {tf.entry_function = {inputs = "input0,input1", outputs = "Add"}} {
  %graph = tf_executor.graph {
    %2:2 = tf_executor.island wraps "tf.Add"(%arg0, %arg1) {T = "tfdtype$DT_INT32", device = ""} : (tensor<10xi32>, tensor<10xi32>) -> tensor<10xi32> loc("Add")
    tf_executor.fetch %2 : tensor<10xi32>
  }
  func.return %graph : tensor<10xi32>
}

>, 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>>>)...

# 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: return %[[GATHER]] : tensor<1x300x10xf32>

// -----

module {
  func.func @propagate_xlagather(%arg0: tensor<10x2xi32>) -> tensor<1x300x10xf32> {
    %cst = "tf.Const"() {value = dense<[1, 1, 300]> : tensor<3xi64>} : () -> tensor<3xi64>

    %4:2 = tf_executor.island wraps "tf.Add"(%arg0, %3) {T = "tfdtype$DT_INT32", device = ""} : (tensor<10xi32>, tensor<10xi32>) -> tensor<10xi32> loc("Add")
    tf_executor.fetch %4#0 : tensor<10xi32>
  }
  func.return %graph : tensor<10xi32>
}

// CHECK: name: "foo"
// CHECK-NEXT: op: "_Arg"
// CHECK: name: "bar"

    return %arg2 : tensor<?xi32>
  }

  // CHECK-LABEL: func @main
  // CHECK-SAME:    %[[ARG0:.*]]: tensor<10xi32>, %[[ARG1:.*]]: tensor<10xi32>
  func.func @main(%arg0: tensor<10xi32>, %arg1: tensor<10xi32>) -> tensor<10xi32> {
    // CHECK:      %[[RESULT:.*]] = "tf.XlaCallModule"(%[[ARG0]], %[[ARG1]])
    // CHECK-SAME:   Sout = [#tf_type.shape<?>]
    // CHECK-SAME:   dim_args_spec = []

    // CHECK:     return %[[RESULT]]
    func.return %0 : tensor<10xi32>
  }

  // CHECK-LABEL: func @foo
  // CHECK-SAME:    %[[ARG0:.*]]: tensor<10xi32>, %[[ARG1:.*]]: tensor<10xi32>
  func.func @foo(%arg0: tensor<10xi32>, %arg1: tensor<10xi32>) -> tensor<10xi32> {

// results, and function body should be the same.

// CHECK-LABEL: module
module {
  // CHECK-LABEL: func @main
  // CHECK-SAME:    %[[ARG0:.*]]: tensor<10xi32>, %[[ARG1:.*]]: tensor<10xi32>
  func.func @main(%arg0: tensor<10xi32>, %arg1: tensor<10xi32>) -> tensor<10xi32> {
    // CHECK:      %[[RESULT:.*]] = "tf.XlaCallModule"(%[[ARG0]], %[[ARG1]])
    // CHECK-SAME:   Sout = [#tf_type.shape<?>]
    // CHECK-NOT:    function_list