def restore_map(self,
                  fine_tune_checkpoint_type='detection',
                  load_all_detection_checkpoint_vars=False):
    """Returns a map of variables to load from a foreign checkpoint.

    Returns a map of variable names to load from a checkpoint to variables in
    the model graph. This enables the model to initialize based on weights from
    another task. For example, the feature extractor variables from a
    classification model can be used to bootstrap training of an object
    detector. When loading from an object detection model, the checkpoint model
    should have the same parameters as this detection model with exception of
    the num_classes parameter.

    Args:
      fine_tune_checkpoint_type: whether to restore from a full detection
        checkpoint (with compatible variable names) or to restore from a
        classification checkpoint for initialization prior to training.
        Valid values: `detection`, `classification`. Default 'detection'.
      load_all_detection_checkpoint_vars: whether to load all variables (when
         `fine_tune_checkpoint_type` is `detection`). If False, only variables
         within the feature extractor scope are included. Default False.

    Returns:
      A dict mapping variable names (to load from a checkpoint) to variables in
      the model graph.
    """
    pass
Returns a map of variables to load from a foreign checkpoint.

    Returns a map of variable names to load from a checkpoint to variables in
    the model graph. This enables the model to initialize based on weights from
    another task. For example, the feature extractor variables from a
    classification model can be used to bootstrap training of an object
    detector. When loading from an object detection model, the checkpoint model
    should have the same parameters as this detection model with exception of
    the num_classes parameter.

    Args:
      fine_tune_checkpoint_type: whether to restore from a full detection
        checkpoint (with compatible variable names) or to restore from a
        classification checkpoint for initialization prior to training.
        Valid values: `detection`, `classification`. Default 'detection'.
      load_all_detection_checkpoint_vars: whether to load all variables (when
         `fine_tune_checkpoint_type` is `detection`). If False, only variables
         within the feature extractor scope are included. Default False.

    Returns:
      A dict mapping variable names (to load from a checkpoint) to variables in
      the model graph.
    
inference_api/src/main/object_detection/core/model.py