%0 = mhlo.custom_call @custom_call.my_custom_op(%arg0) {
    api_version = 1 : i32,
    backend_config = "this_is_a_test_string"
  } : (tensor<1x4xf32>) -> (tensor<1x8xf32>)
  func.return %0 : tensor<1x8xf32>

  //       CHECK: %0 = "tfl.custom"(%arg0) <{
  //  CHECK-SAME:   custom_code = "custom_call.my_custom_op",

// CHECK: error: 'tf.MyCustomOp' op is neither a custom op nor a flex op
// CHECK: error: failed while converting: 'main'
// CHECK: Some ops in the model are custom ops, See instructions to implement
// CHECK: tf.MyCustomOp(tensor<4xf32>, tensor<4xf32>) -> (tensor<4xf32>, tensor<3xf32>) : {name = "MyCustomOp"}

func.func @main(tensor<4xf32>) -> tensor<4xf32> {
^bb0(%arg0: tensor<4xf32>):

  // will be preserved since it has uses.
  %2 = "tf.MyCustomOp"(%1, %0) {fused_activation_function = "RELU", int_attr = 2 : i32}  : (tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32>
  // will be preserved since it has side-effect.
  "tf.MyCustomOp"(%1, %0) {fused_activation_function = "RELU", int_attr = 2 : i32}  : (tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32>

  %1 = "tfl.mul"(%arg0, %0) {fused_activation_function = "NONE"} : (tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32> loc("mul")
  // tf.MyCustomOp is the result of conversion to a Custom op
  %2 = "tf.MyCustomOp"(%1, %0) {fused_activation_function = "RELU", int_attr = 2 : i32}  : (tensor<4xf32>, tensor<4xf32>) -> tensor<4xf32> loc("MyCustomOp")
  %3 = "tfl.exp"(%2)  : (tensor<4xf32>) -> tensor<4xf32> loc("exp")
  func.return %3 : tensor<4xf32>

    // CHECK: "tf.VarHandleOp"() <{container = "", shared_name = "bar"}> : () -> tensor<!tf_type.resource<tensor<*xi8>>>
    %read = "tf.ReadVariableOp"(%0) : (tensor<!tf_type.resource<tensor<*xi8>>>) -> tensor<1xi8>
    %1 = "tf.MyCustomOp"(%0) : (tensor<!tf_type.resource<tensor<*xi8>>>) -> tensor<4xi8>
    func.return %read : tensor<1xi8>
  }

  // CHECK-LABEL: testXlaConvV2OpInvalidFeatureGroupCount