func.return %arg0: tensor<1x!tf_type.qint8>
}

// -----

// CHECK-LABEL: func @id_qint16(%arg0: tensor<1xi16>) -> tensor<1xi16> {
func.func @id_qint16(%arg0: tensor<1x!tf_type.qint16>) -> tensor<1x!tf_type.qint16> {
  // CHECK-NEXT: return %arg0 : tensor<1xi16>
  func.return %arg0: tensor<1x!tf_type.qint16>
}

// -----

// CHECK-LABEL: func @id_qint32(%arg0: tensor<1xi32>) -> tensor<1xi32> {

  %15 = mhlo.compare  LT, %13, %14,  SIGNED : (tensor<4xi32>, tensor<4xi32>) -> tensor<4xi1>
  %16 = "mhlo.broadcast_in_dim"(%0) <{broadcast_dimensions = dense<> : tensor<0xi64>}> : (tensor<i32>) -> tensor<4xi32>
  %17 = mhlo.add %13, %16 : tensor<4xi32>
  %18 = mhlo.select %15, %17, %13 : tensor<4xi1>, tensor<4xi32>

  // 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

}

func.func @broadcast_to_i16_low_dim(%input: tensor<3xi16>, %shape: tensor<2xi32>) -> tensor<3x3xi16> {
  %0 = "tf.BroadcastTo"(%input, %shape) : (tensor<3xi16>, tensor<2xi32>) -> tensor<3x3xi16>
  func.return %0: tensor<3x3xi16>

// CHECK-LABEL: broadcast_to_i16_low_dim
// CHECK:    %0 = "tf.BroadcastTo"(%arg0, %arg1) : (tensor<3xi16>, tensor<2xi32>) -> tensor<3x3xi16>
// CHECK:    return %0 : tensor<3x3xi16>
}

  func.return %0 : tensor<?xf32>
}

// -----

// CHECK-LABEL: testGatherNdI16Indices
func.func @testGatherNdI16Indices(%arg0 : tensor<?xf32>, %arg1 : tensor<?xi16>) -> tensor<?xf32> {
  %0 = "tfl.gather_nd"(%arg0, %arg1) {axis = 1 : i32}: (tensor<?xf32>,tensor<?xi16>) -> tensor<?xf32>
  func.return %0 : tensor<?xf32>
}

// -----

// CHECK-LABEL: testAbs
func.func @testAbs(tensor<? x f32>) -> tensor<? x f32> {

func.func @broadcast_to_i16_low_dim(%arg0: tensor<3xi16>, %arg1: tensor<2xi32>) -> tensor<3x3xi16> {
  %0 = "tfl.broadcast_to"(%arg0, %arg1) : (tensor<3xi16>, tensor<2xi32>) -> tensor<3x3xi16>
  return %0 : tensor<3x3xi16>
  // CHECK:  %cst = arith.constant dense<1> : tensor<3x3xi16>
  // CHECK:  %0 = tfl.mul(%arg0, %cst) <{fused_activation_function = "NONE"}> : (tensor<3xi16>, tensor<3x3xi16>) -> tensor<3x3xi16>

  func.return %4#0, %4#1 : tensor<4x32xf32>, tensor<4x32xi32>
}

// CHECK-LABEL:   func @convert_argmin_i16(
// CHECK-SAME:                         %[[VAL_0:.*]]: tensor<2xi16>) -> (tensor<i16>, tensor<i32>) {
// CHECK:           %[[VAL_9:.*]] = arith.constant dense<0> : tensor<1xi32>
// CHECK:           %[[VAL_10:.*]] = "tf.Min"(%[[VAL_0]], %[[VAL_9]]) <{keep_dims = false}> : {{.*}} -> tensor<i16>