output_types = [f32],
        metadata = ""
      } :
         (tensor<!tf_type.string>, tensor<*x!tf_type.string>, tensor<!tf_type.string>, tensor<*xi64>, tensor<!tf_type.string>) -> tensor<*x!tf_type.variant>
      %add1 = "tf.Add"(%str, %arg3) : (tensor<!tf_type.string>, tensor<!tf_type.string>) -> tensor<!tf_type.string>
      // CHECK: tf_executor.island(%[[CONTROL]]) wraps "tf.GeneratorDataset"

(AtomicCompareAndSwap(32|64) ...) => (LoweredAtomicCas(32|64)      ...)

(AtomicAdd(32|64)Variant            ...) => (LoweredAtomicAdd(32|64)Variant      ...)
(AtomicExchange(32|64)Variant       ...) => (LoweredAtomicExchange(32|64)Variant ...)
(AtomicCompareAndSwap(32|64)Variant ...) => (LoweredAtomicCas(32|64)Variant      ...)

// Return old contents.

  %5 = "tf.Identity"(%4) {__op_key = 10: i32, device = "/device:CPU:0"} : (tensor<!tf_type.variant>) -> tensor<!tf_type.variant>
  func.return %5 : tensor<!tf_type.variant>
}
// CHECK-LABEL: __inference_Dataset_map_lambda_160

  func.func @main(%arg0: tensor<!tf_type.resource> {tf.device = "/job:localhost/replica:0/task:0/device:CPU:0"}, %arg1: tensor<!tf_type.variant> {tf.device = "/job:localhost/replica:0/task:0/device:CPU:0"}, %arg2: tensor<!tf_type.resource> {tf.device = "/job:localhost/replica:0/task:0/device:CPU:0"}, %arg3: tensor<!tf_type.variant> {tf.device = "/job:localhost/replica:0/task:0/device:CPU:0"}, %arg4: tensor<!tf_type.resource<tensor<7x7x3x64xf32>>> {tf.device = "/job:localhost/repl...

        val adhocComponent = softwareComponentFactory.adhoc("myAdhocComponent")
        // add it to the list of components that this project declares
        components.add(adhocComponent)
        // and register a variant for publication
        adhocComponent.addVariantsFromConfiguration(outgoing) {
            mapToMavenScope("runtime")
        }
        // end::create_adhoc_component[]
    }

}

def BatchFunctionOp : TensorflowMlrt_Op<"batch_function", [Pure]> {
  let summary = "Fallback ExecuteOp specialized for tf.BatchFunction.";

  let description = [{
    This kernel executes a variant tf.BatchFunction kernel that supports having
    the `f` attribute as a bytecode function.

    Example:
      %res = tf_mlrt.batch_function(%input, %captured_input)  {
          device = "/device:CPU:0",

   * response code is 407 proxy unauthorized, this returns the "Proxy-Authenticate" challenges.
   * Otherwise this returns an empty list of challenges.
   *
   * If a challenge uses the `token68` variant instead of auth params, there is exactly one
   * auth param in the challenge at key null. Invalid headers and challenges are ignored.
   * No semantic validation is done, for example that `Basic` auth must have a `realm`

    %9 = "tf.opK"(%input_0, %input_4, %9) :
      (tensor<i32>, tensor<!tf_type.variant>, tensor<i1>) -> tensor<i32>
    tf_device.return %9 : tensor<i32>
  }) {device = "DEVICE_ALIAS_0"} : () -> tensor<i32>
  %l = "tf_device.launch"() ( {
    %10 = "tf.opL"(%input_1, %input_4, %9) :
      (tensor<f32>, tensor<!tf_type.variant>, tensor<i1>) -> tensor<f32>
    tf_device.return %10 : tensor<f32>

org.ow2.asm:asm:7.1
  Variant compile:
    | Attribute Name                 | Provided | Requested    |
    |--------------------------------|----------|--------------|
    | org.gradle.status              | release  |              |
    | org.gradle.category            | library  | library      |
    | org.gradle.libraryelements     | jar      | classes      |
    | org.gradle.usage               | java-api | java-api     |

     file or files, that is not Covered Software.

1.8. “License”

     means this document.

1.9. “Licensable”

     means having the right to grant, to the maximum extent possible, whether at the
     time of the initial grant or subsequently, any and all of the rights conveyed by
     this License.

1.10. “Modifications”

     means any of the following: