tensorflow::MarkForCompilationPassFlags* flags =
        tensorflow::GetMarkForCompilationPassFlags();
    flags->tf_xla_cpu_global_jit = true;
    flags->tf_xla_min_cluster_size = 1;
  } else {
    optimizer_options->set_global_jit_level(tensorflow::OptimizerOptions::OFF);
  }
}

unsigned char TF_SetXlaEnableLazyCompilation(unsigned char enable) {
  tensorflow::BuildXlaOpsPassFlags* flags =

            "@com_google_absl//absl/types:variant",
        ],
    }) + select({
        "//tensorflow:with_xla_support": [
            "//tensorflow/compiler/jit",
            "//tensorflow/compiler/jit:xla_device",
            "//tensorflow/compiler/tf2xla:xla_compiler",
        ],
        "//conditions:default": [],
    }) + [
        "//tensorflow/c:tf_status_helper",

tfrt/ir,tensorflow/compiler/mlir/tfrt/ir/mlrt,tensorflow/compiler/mlir/tfrt/jit/python_binding,tensorflow/compiler/mlir/tfrt/jit/transforms,tensorflow/compiler/mlir/tfrt/python_tests,tensorflow/compiler/mlir/tfrt/tests,tensorflow/compiler/mlir/tfrt/tests/ifrt,tensorflow/compiler/mlir/tfrt/tests/mlrt,tensorflow/compiler/mlir/tfrt/tests/ir,tensorflow/compiler/mlir/tfrt/tests/analysis,tensorflow/compiler/mlir/tfrt/tests/jit,tensorflow/compiler/mlir/tfrt/tests/lhlo_to_tfrt,tensorflow/compiler/mlir/t...

        `ParameterServerStrategy` or with interactive logging disabled.
    *   Argument `jit_compile` in `Model.compile()` now applies to
        `Model.evaluate()` and `Model.predict()`. Setting `jit_compile=True` in
        `compile()` compiles the model's training, evaluation, and inference
        steps to [XLA](https://www.tensorflow.org/xla). Note that
        `jit_compile=True` may not necessarily work for all models.