// CHECK-SAME:                        %[[VAL_1:.*]]: tensor<1x2xi1>) -> tensor<1x2xi1> {
// CHECK:           %[[VAL_2:.*]] = "tf.LogicalAnd"(%[[VAL_0]], %[[VAL_1]]) : (tensor<1xi1>, tensor<1x2xi1>) -> tensor<1x2xi1>
// CHECK:           return %[[VAL_2]] : tensor<1x2xi1>
// CHECK:         }
func.func @and_broadcast(%arg0: tensor<1xi1>, %arg1: tensor<1x2xi1>) -> tensor<1x2xi1> {

}

// CHECK-LABEL: EliminateRedundantLogicalOr
func.func @EliminateRedundantLogicalOr(%arg0: tensor<1xi1>, %arg1: tensor<2x3xi1>) -> (tensor<1xi1>, tensor<2x3xi1>, tensor<2x3xi1>, tensor<2x3xi1>, tensor<2x3xi1>, tensor<2x3xi1>, tensor<1xi1>, tensor<2x3xi1>, tensor<2x3xi1>, tensor<2x3xi1>, tensor<2x3xi1>, tensor<2x3xi1>, tensor<2x3xi1>, tensor<2x3xi1>) {
  %cst_false0 = arith.constant dense<false> : tensor<1xi1>

// -----

// CHECK-LABEL: @if
func.func @if() {
  // CHECK-NOT: Optional
  %0 = builtin.unrealized_conversion_cast to tensor<5xi1>
  %1 = "tf.OptionalNone"() : () -> tensor<!tf_type.variant<tensor<f32>>>
  %2 = "tf.If"(%0, %1) <{else_branch = @false, is_stateless = false, then_branch = @true}>
      : (tensor<5xi1>, tensor<!tf_type.variant<tensor<f32>>>) -> (tensor<!tf_type.variant<tensor<f32>>>)
  return
}

}

func.func @concat_v2_with_bool_type(%arg0: tensor<?x1xi1>, %arg1: tensor<?x1xi1>) -> tensor<?x2xi1> {
  %0 = "tf.Const"() { value = dense<-1> : tensor<i32> } : () -> tensor<i32>
  %1 = "tf.ConcatV2"(%arg0, %arg1, %0) : (tensor<?x1xi1>, tensor<?x1xi1>, tensor<i32>) -> tensor<?x2xi1>
  func.return %1 : tensor<?x2xi1>

// CHECK-LABEL: concat_v2_with_bool_type

  module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 268 : i32}} {
  func.func @main() -> () {
    %cst0 = "tf.Const"(){ value = dense<0> : tensor<3x5xi1>} : () -> tensor<3x5xi1>
    %0 = "tf.Where"(%cst0) : (tensor<3x5xi1>) -> tensor<?x2xi64>
    func.return
  }
})";

absl::StatusOr<XlaCompiler::CompilationResult> CompileMlirModule(
    const char* mlir_module_str,

  // CHECK-SAME: %[[ARG0:.*]]: tensor<1x2xf32>
  // CHECK-SAME: %[[ARG1:.*]]: tensor<2x3xf32>
  // CHECK-SAME: %[[ARG2:.*]]: tensor<1x3xi1>
  // CHECK-SAME: %[[ARG3:.*]]: tensor<1x3xf32>
  func.func private @composite_and_select(%arg0: tensor<1x2xf32>, %arg1: tensor<2x3xf32>, %arg2: tensor<1x3xi1>, %arg3: tensor<1x3xf32>) -> tensor<1x3xf32> {

func.func @select(
    %arg0: tensor<1x3xi1>,
    %arg1: tensor<1x3x!quant.uniform<i8:f32, 2.000000e+00:-1>>,
    %arg2: tensor<1x3x!quant.uniform<i8:f32, 2.000000e+00:-1>>
  ) -> tensor<1x3x!quant.uniform<i8:f32, 2.000000e+00:-1>> {
  %0 = "stablehlo.select"(%arg0, %arg1, %arg2) : (
    tensor<1x3xi1>,
    tensor<1x3x!quant.uniform<i8:f32, 2.000000e+00:-1>>,

}

// -----

func.func @testSelectV2WithWrongBroadcastableArguments(%cond : tensor<3x4xi1>, %arg0 : tensor<2x3x4xf32>, %arg1 : tensor<4x3xf32>) -> tensor<2x3x4xf32> {
  // expected-error @+1 {{'tfl.select_v2' op operands don't have broadcast-compatible shapes}}
  %0 = "tfl.select_v2"(%cond, %arg0, %arg1): (tensor<3x4xi1>, tensor<2x3x4xf32>, tensor<4x3xf32>) -> tensor<2x3x4xf32>
  func.return %0 : tensor<2x3x4xf32>
}