code: def _resolve_auto_jit_compile(self):
      if backend.backend() == "torch":
          # jit_compile = "auto" with the pytorch backend defaults to eager
          return False
  
      if backend.backend() == "tensorflow":
          import tensorflow as tf
  
          devices = tf.config.list_physical_devices()
          if not list(
              filter(lambda x: x.device_type != "CPU", devices)
          ):
              # Disable XLA on CPU-only machines.
              return False
  
          if self._distribute_strategy:
              # Disable XLA with tf.distribute
              return False
  
      if model_supports_jit(self):
          return True
      return False
  
imports: import platform
  import warnings
  import tree
  from keras import backend
  from keras import metrics
  from keras import ops
  from keras import optimizers
  from keras.optimizers.loss_scale_optimizer import LossScaleOptimizer
  from keras.saving import serialization_lib
  from keras.trainers.compile_utils import CompileLoss
  from keras.trainers.compile_utils import CompileMetrics
  from keras.trainers.data_adapters import data_adapter_utils
  from keras.utils import traceback_utils
  from keras.utils import tracking
label_desc: Use image node_id 5 for calling the Trainer obj's underlying member method code with example usage: obj._resolve_auto_jit_compile() and returns: False, False, True, False, False