}


// -----

func.func @transpose_perm_size(%arg0 : tensor<2x2xi32>, %arg1 : tensor<3xi32>) -> tensor<2x2xi32> {
  // expected-error @+1 {{perm tensor elements size is not equal to input tensor rank}}
  %0 = "tfl.transpose"(%arg0, %arg1) : (tensor<2x2xi32>, tensor<3xi32>) -> tensor<2x2xi32>
  func.return %0 : tensor<2x2xi32>
}


// -----

    %11 = stablehlo.convert %3 : (tensor<1x3xi32>) -> tensor<1x3xf32>
    %12 = stablehlo.subtract %10, %11 : tensor<1x3xf32>  // q1 * q2 - z1 * q2
    %13 = stablehlo.multiply %12, %4 : tensor<1x3xf32>  // s1 * s2
    %14 = call @uniform_quantize_1(%13, %5, %6) : (tensor<1x3xf32>, tensor<1x1xf32>, tensor<1x1xi8>) -> tensor<1x3xi8>

// CHECK:           %[[VAL_12:.*]] = "tf.AddV2"(%[[VAL_8]], %[[VAL_11]]) : (tensor<3xi32>, tensor<2x3xi32>) -> tensor<2x3xi32>
// CHECK:           %[[VAL_13:.*]] = "tf.Neg"(%[[VAL_12]]) : (tensor<2x3xi32>) -> tensor<2x3xi32>
// CHECK:           %[[VAL_14:.*]] = "tf.Abs"(%[[VAL_1]]) : (tensor<2x3xi32>) -> tensor<2x3xi32>

    %10 = "tf.Cast"(%9) {Truncate = false, device = ""} : (tensor<1x3xi32>) -> tensor<1x3xf32>
    %11 = "tf.Mul"(%10, %cst) {device = ""} : (tensor<1x3xf32>, tensor<f32>) -> tensor<1x3xf32>
    %12 = "tf.Relu"(%11) {device = ""} : (tensor<1x3xf32>) -> tensor<1x3xf32>
    return %12 : tensor<1x3xf32>
  }
// CHECK-LABEL: func @matmul_with_relu

  // CHECK-DAG: %[[INDICES_3:.*]] = "tf.Mul"(%[[INDICES_2]], %arg0) : (tensor<5xi32>, tensor<5xi32>) -> tensor<5xi32>
  // CHECK-DAG: %[[INDICES_4:.*]] = "tf.TensorScatterAdd"(%[[cst_3]], %0, %[[UPDATES]]) : (tensor<5xi32>, tensor<5x1xi32>, tensor<5xi32>) -> tensor<5xi32>
  // CHECK-DAG: %[[INDICES_5:.*]] = "tf.AddV2"(%[[INDICES_3]], %[[INDICES_4]]) : (tensor<5xi32>, tensor<5xi32>) -> tensor<5xi32>

}
// CHECK: func private @callee_tensorarray_decomposed(%[[CARG0:.*]]: tensor<!tf_type.resource<tensor<5x3xf32>>>, %[[CARG1:.*]]: tensor<!tf_type.resource<tensor<5x3xf32>>>, %[[CARG2:.*]]: tensor<!tf_type.resource<tensor<5x3xf32>>>)
// CHECK: %[[READ1:.*]] = "tf.ReadVariableOp"(%[[CARG1]]) : (tensor<!tf_type.resource<tensor<5x3xf32>>>) -> tensor<5x3xf32>
// CHECK: %[[UPDATE1:.*]] = "tf.XlaDynamicUpdateSlice"(%[[READ1]],

  "tf_saved_model.global_tensor"() {is_mutable, sym_name = "y", type = tensor<3x1xi32>, value = dense<[[1], [2], [3]]> : tensor<3x1xi32>} : () -> ()
  "tf_saved_model.asset"() {sym_name = "z", filename = "file"} : () -> ()
  func.func @serving_default(
      %arg0: tensor<1x3xi32> {tf_saved_model.index_path = ["x"]},
      %arg1: tensor<!tf_type.resource<tensor<3x1xi32>>> {tf_saved_model.bound_input = @y},

func.func @tensorlistConst(%arg0 : tensor<1xi32>) -> tensor<2x3xi32> {
  // CHECK-DAG: %[[ELEMENT0:.*]] = "tf.Const"() <{value = dense<[0, 1, 2]> : tensor<3xi32>}> : () -> tensor<3xi32>
  // CHECK-DAG: %[[ELEMENT1:.*]] = "tf.Const"() <{value = dense<[3, 4, 5]> : tensor<3xi32>}> : () -> tensor<3xi32>
  // CHECK: %[[LIST:.*]] = "tf.Pack"(%[[ELEMENT0]], %[[ELEMENT1]]) <{axis = 0 : i64}> : (tensor<3xi32>, tensor<3xi32>) -> tensor<2x3xi32>

func.func @my_add_n_single_input(%arg0: tensor<2x3xf32>) -> tensor<2x3xf32> {
  %0 = "tf.MyAddN"(%arg0) {N=1:i32} : (tensor<2x3xf32>) -> tensor<2x3xf32>
  func.return %0 : tensor<2x3xf32>

// CHECK-NEXT: return %arg0 : tensor<2x3xf32>
}

// CHECK-LABEL: my_add_n_multiple_inputs
func.func @my_add_n_multiple_inputs(%arg0: tensor<2x3xf32>, %arg1: tensor<2x3xf32>, %arg2: tensor<2x3xf32>) -> tensor<2x3xf32> {

  } : (tensor<1x2xf32>, tensor<2x3xf32>) -> tensor<1x3xf32>
  return %0 : tensor<1x3xf32>
}

// CHECK-LABEL: func.func @qdq_for_dot_general_weight_empty
// CHECK-SAME: (%[[ARG_0:.+]]: tensor<1x2xf32>) -> tensor<1x3xf32>
// CHECK: %[[CST:.+]] = "tf.Const"() <{value = dense<3.000000e-01> : tensor<2x3xf32>}> : () -> tensor<2x3xf32>