f = @identity} : (tensor<!tf_type.variant<tensor<f32>>>) -> tensor<!tf_type.variant<tensor<f32>>>
  %2 = "tf.OptionalGetValue"(%1) : (tensor<!tf_type.variant<tensor<f32>>>) -> tensor<f32>
  %3 = "tf.OptionalNone"() : () -> tensor<!tf_type.variant<tensor<f32>>>
  return %2, %3 : tensor<f32>, tensor<!tf_type.variant<tensor<f32>>>
}

func.func private @identity(%arg0: tensor<!tf_type.variant<tensor<f32>>>) -> tensor<!tf_type.variant<tensor<f32>>> {

            writeVariant(it)
        }
    }

    private
    suspend fun WriteContext.writeVariant(variant: VariantResolveMetadata) {
        writeString(variant.name)
        write(variant.identifier)
        write(variant.attributes)
        writeCollection(variant.artifacts)
    }

    override fun read(decoder: Decoder): LocalComponentGraphResolveState {

    the<com.android.build.gradle.AppExtension>().compileSdkVersion(30)
  }

  var expectedClasspath =
    if (runningGradle5) {
      // without Gradle Module Metadata (only the POM is used)
      // - variant decision is made based on version suffix (android/jre) and not on the actual
      // environment
      // - runtime classpath equals the compile classpath
      // - dependency conflict with Google Collections is not detected

  string hub = 2;

  // The container image tag to pull. Image will be `Hub/Image:Tag-Variant`.
  google.protobuf.Value tag = 3;

  // The container image variant to pull. Options are "debug" or "distroless". Unset will use the default for the given version.
  string variant = 29;

  // Image name to pull from. Image will be `Hub/Image:Tag-Variant`.

        %3 = "tf.OptionalHasValue"(%2) : (tensor<!tf_type.variant>) -> tensor<i1>
        %4 = "tf.IfRegion"(%3) ({
          %5 = "tf.OptionalGetValue"(%2) : (tensor<!tf_type.variant>) -> tensor<32xf32>

        val variant = candidates.variants.first()
        return when (val spec = transforms[variant.attributes.asImmutable()]) {
            null -> {
                // no mapping for extension, so it can be discarded
                if (matchingOnArtifactFormat) {
                    ResolvedArtifactSet.EMPTY
                } else {
                    variant.artifacts
                }
            }

  %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

}

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",

     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:

    a separate 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: