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 _compute_dummy_pred_proba(self, y):
      num_classes = self.num_classes
      if self.problem_type in [BINARY, MULTICLASS]:
          dummies = pd.get_dummies(y)
          dummy_columns = set(list(dummies.columns))
          if len(dummies.columns) < num_classes:
              for c in range(num_classes):
                  if c not in dummy_columns:
                      dummies[c] = 0
          dummies = dummies[list(range(num_classes))]
          pred_proba_mean = dummies.mean().values
  
      elif self.problem_type in [REGRESSION, QUANTILE, SOFTCLASS]:
          pred_proba_mean = y.mean()
      else:
          raise NotImplementedError(
              f"Computing dummy pred_proba is not implemented for {self.problem_type}."
          )
      return pred_proba_mean
  
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 7 for calling the ImagePredictorModel obj's underlying member method code with example usage: obj._compute_dummy_pred_proba(y) and returns: pred_proba_mean