class_docstr: MultimodalPredictor that only uses image features.
  Currently only supports 1 image column, with 1 image per sample.
  Additionally has special null image handling to improve performance in the presence of null images (aka image path of '')
      Note: null handling has not been compared to the built-in null handling of MultimodalPredictor yet.
code: def _get_default_auxiliary_params(self) -> dict:
      default_auxiliary_params = super()._get_default_auxiliary_params()
      extra_auxiliary_params = dict(
          get_features_kwargs=dict(
              valid_raw_types=[R_OBJECT],
              required_special_types=[S_IMAGE_PATH],
          ),
      )
      default_auxiliary_params.update(extra_auxiliary_params)
      return default_auxiliary_params
  
file_path: autogluon/tabular/src/autogluon/tabular/models/image_prediction/image_predictor.py
imports: import logging
  import numpy
  import pandas
  from autogluon.common.features.types import R_OBJECT, S_IMAGE_PATH
  from autogluon.core.constants import BINARY, MULTICLASS, QUANTILE, REGRESSION, SOFTCLASS
  from ..automm.automm_model import MultiModalPredictorModel
label_desc: Use image node_id 3 for calling the ImagePredictorModel obj's underlying member method code with example usage: obj._get_default_auxiliary_params() and returns: default_auxiliary_params