class_docstr: Class to loads TF v1 and TF v2 hub modules that could be fine-tuned.
  
  Since TF v1 modules couldn't be retrained in hub.KerasLayer. This class
  provides a workaround for retraining the whole tf1 model in tf2. In
  particular, it extract self._func._self_unconditional_checkpoint_dependencies
  into trainable variable in tf1.
  
  Doesn't update moving-mean/moving-variance for BatchNormalization during
  fine-tuning.
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code: def _check_trainability(self):
      if self._is_hub_module_v1:
          self._check_trainability_v1()
      else:
          # call _check_trainability from the base class for v2.
          super(HubKerasLayerV1V2, self)._check_trainability()
  
imports: import tensorflow_hub
label_desc: Use image node_id 2 for calling the HubKerasLayerV1V2 obj's underlying member method code with example usage: obj._check_trainability() without return types