// CHECK:  %cst = arith.constant dense<0> : tensor<1xi32>
  // CHECK:  %4 = "tfl.reduce_min"(%arg0, %cst) <{keep_dims = false}> : (tensor<2xi16>, tensor<1xi32>) -> tensor<i16>
  // CHECK:  %5 = "tfl.arg_min"(%arg0, %cst) : (tensor<2xi16>, tensor<1xi32>) -> tensor<i32>
  // CHECK:  return %4, %5 : tensor<i16>, tensor<i32>
}


// CHECK-LABEL: func @convert_argmin_constant

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

// CHECK: name: "const2"
// CHECK-NEXT: op: "Const"
// CHECK: dtype: DT_HALF
// CHECK: half_val: 15360
// CHECK-NEXT: half_val: 16384
    %2:2 = tf_executor.island wraps "tf.Const"() {device = "", dtype = bf16, value = dense<[4.900000e+01, 8.200000e+02]> : tensor<2xbf16>} : () -> tensor<bf16> loc("const3")

  %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.func @main() -> (tensor<1x2xf16>, tensor<2xf16>) {
  %graph:2 = tf_executor.graph {
    %0:2 = tf_executor.island wraps "tf.Const"() {device = "", dtype = "tfdtype$DT_HALF", value = dense<1.0> : tensor<1x2xf16>} : () -> tensor<1x2xf16> loc("const1")
    %1:2 = tf_executor.island wraps "tf.Const"() {device = "", dtype = "tfdtype$DT_HALF", value = dense<[1.0, 2.0]> : tensor<2xf16>} : () -> tensor<2xf16> loc("const2")

func.func @main() -> (tensor<1x2xf16>, tensor<2xf16>) {
  %graph:2 = tf_executor.graph {
    %0:2 = tf_executor.island wraps "tf.Const"() {device = "", dtype = "tfdtype$DT_HALF", value = dense<1.0> : tensor<1x2xf16>} : () -> tensor<1x2xf16> loc("const1")
    %1:2 = tf_executor.island wraps "tf.Const"() {device = "", dtype = "tfdtype$DT_HALF", value = dense<[1.0, 2.0]> : tensor<2xf16>} : () -> tensor<2xf16> loc("const2")

  func.return %0 : tensor<2x4xui8>
}

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

// CHECK-LABEL: func @and_unranked
func.func @and_unranked(%arg0: tensor<*xi1>, %arg1: tensor<*xi1>) -> tensor<*xi1> {

  func.return %0 : tensor<4xi8>
}

func.func @i16() -> tensor<4xi16> {
  // CHECK-LABEL: @i16
  // CHECK: value = dense<[1, 2, 3, 258]> : tensor<4xi16>
  %0 = "tfl.pseudo_const" () { value = dense<[1, 2, 3, 258]> : tensor<4xi16> } : () -> tensor<4xi16>
  func.return %0 : tensor<4xi16>
}

func.func @i32() -> tensor<4xi32> {
  // CHECK-LABEL: @i32

  func.return %0 : tensor<4xi8>
}

func.func @i16() -> tensor<4xi16> {
  // CHECK-LABEL: @i16
  // CHECK: value = dense<[1, 2, 3, 258]> : tensor<4xi16>
  %0 = "tfl.pseudo_const" () { value = dense<[1, 2, 3, 258]> : tensor<4xi16> } : () -> tensor<4xi16>
  func.return %0 : tensor<4xi16>
}

func.func @i32() -> tensor<4xi32> {
  // CHECK-LABEL: @i32

}

// CHECK-LABEL: fixed_element_attribute
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>