%a = corert.const_dense_tensor dense<[true, false]> : tensor<2xi1>
  %b = corert.const_dense_tensor dense<[false, true]> : tensor<2xi1>
  // CHECK: [[b:%.*]] = tfrt_fallback_async.const_dense_tensor dense<[false, true]> : tensor<2xi1>
  // CHECK-NEXT:      [[a:%.*]] = tfrt_fallback_async.const_dense_tensor dense<[true, false]> : tensor<2xi1>
  // CHECK-NEXT: tfrt_fallback_async.corert_tensorhandle_to_fallback_tensor [[arg0]]

  func.return %0: tensor<*xi1>
}

// CHECK-LABEL: func @or
func.func @or(%arg0: tensor<2xi1>, %arg1: tensor<2xi1>) -> tensor<2xi1> {
  // CHECK-NEXT:  mhlo.or
  %0 = "tf.LogicalOr"(%arg0, %arg1) : (tensor<2xi1>, tensor<2xi1>) -> tensor<2xi1>
  func.return %0: tensor<2xi1>
}

// CHECK-LABEL: func @bitwise_or
func.func @bitwise_or(%arg0: tensor<4xi32>, %arg1: tensor<4xi32>) -> tensor<4xi32> {

func.func @fixed_element_attribute(%arg0: tensor<2xf32>) -> tensor<2xi1> {
  %0 = "tfr.cast"(%arg0) : (tensor<2xf32>) -> !tfr.tensor
  %1 = tfr.call @tf__positive(%0) : (!tfr.tensor) -> !tfr.tensor
  %2 = "tfr.cast"(%1) : (!tfr.tensor) -> tensor<2xi1>
  func.return %2 : tensor<2xi1>
// CHECK: %[[positive:.*]] = "tf.Positive"(%arg0) : (tensor<2xf32>) -> tensor<*xi1>

// CHECK:           %[[VAL_2:.*]] = "tf.LogicalOr"(%[[VAL_0]], %[[VAL_1]]) : (tensor<2xi1>, tensor<2xi1>) -> tensor<2xi1>
// CHECK:           return %[[VAL_2]] : tensor<2xi1>
// CHECK:         }
func.func @or(%arg0: tensor<2xi1>, %arg1: tensor<2xi1>) -> tensor<2xi1> {
  %0 = mhlo.or %arg0, %arg1 : tensor<2xi1>
  func.return %0 : tensor<2xi1>
}

// CHECK-LABEL:   func @or_broadcast(

// CHECK-LABEL:   func.func @convert_argmax_bool
func.func @convert_argmax_bool(%arg0: tensor<2xi1>) -> tensor<i32> {
  %0 = "mhlo.iota"() <{iota_dimension = 0 : i64}> : () -> tensor<2xi32>
  %1 = mhlo.constant dense<false> : tensor<i1>
  %2 = mhlo.constant dense<0> : tensor<i32>
  %3:2 = mhlo.reduce(%arg0 init: %1), (%0 init: %2) across dimensions = [0] : (tensor<2xi1>, tensor<2xi32>, tensor<i1>, tensor<i32>) -> (tensor<i1>, tensor<i32>)

  // CHECK: %[[AND:.*]] = "tf.LogicalAnd"(%[[EQ]], %[[IS_ODD]]) : (tensor<2xi1>, tensor<2xi1>) -> tensor<2xi1>
  // CHECK: %[[OR:.*]] = "tf.LogicalOr"(%[[GT]], %[[AND]]) : (tensor<2xi1>, tensor<2xi1>) -> tensor<2xi1>
  // CHECK: %[[ADD:.*]] = "tf.AddV2"(%[[ROUND_VAL]], %[[ONE]]) : (tensor<2xf32>, tensor<f32>) -> tensor<2xf32>

func.func @testLogicalAndDoesntFoldWithConstantFalseBroadcast(%arg0: tensor<2xi1>) -> (tensor<2xi1>) {
  %cst = arith.constant dense<false> : tensor<i1>

  // CHECK: %[[LOGICAL_AND:.*]] = "tf.LogicalAnd"
  %0 = "tf.LogicalAnd"(%cst, %arg0) : (tensor<i1>, tensor<2xi1>) -> tensor<2xi1>

  // CHECK: return %[[LOGICAL_AND]]
  func.return %0: tensor<2xi1>
}

// -----

// GlobalIterId should not be constant folded

  }

  // CHECK-LABEL: func @greater
  func.func @greater(%arg0: tensor<2xi32>, %arg1: tensor<2xi32>) -> tensor<2xi1> {
    // CHECK-NEXT:  mhlo.compare GT, %arg0, %arg1
    %0 = "tf.Greater"(%arg0, %arg1) : (tensor<2xi32>, tensor<2xi32>) -> tensor<2xi1>
    func.return %0: tensor<2xi1>
  }

  // CHECK-LABEL: batchmatmulv2

}

// CHECK-LABEL: @cast_i1_to_i8
func.func @cast_i1_to_i8() -> tensor<2xi8> {
  %cst = arith.constant dense<[false, true]> : tensor<2xi1>
  %0 = "tfl.cast"(%cst) : (tensor<2xi1>) -> tensor<2xi8>
  func.return %0 : tensor<2xi8>

// CHECK: %[[CST:.*]] = arith.constant dense<[0, 1]> : tensor<2xi8>
// CHECK:  return %[[CST]]
}

// CHECK-LABEL: @cast_i1_to_ui8

    %26 = "tf.Cast"(%25) {Truncate = false} : (tensor<2xi64>) -> tensor<2xf32>
    %27 = "tf.Equal"(%14, %26) {incompatible_shape_error = true} : (tensor<2xf32>, tensor<2xf32>) -> tensor<2xi1>
    %28 = "tf.Cast"(%27) {Truncate = false} : (tensor<2xi1>) -> tensor<2xf32>
    %29 = "tf.Sum"(%28, %6) {keep_dims = false} : (tensor<2xf32>, tensor<1xi32>) -> tensor<f32>
    %30 = "tf.CrossReplicaSum"(%29, %3) : (tensor<f32>, tensor<1x2xi32>) -> tensor<f32>