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 _has_predict_proba(self):
      return self.problem_type in [BINARY, MULTICLASS, SOFTCLASS]
  
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 2 for calling the ImagePredictorModel obj's underlying member method code with example usage: obj._has_predict_proba() and returns: unknown