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 __init__(self, **kwargs):
      super().__init__(**kwargs)
      self._dummy_pred_proba = (
          None  # Dummy value to predict if image is NaN
      )
      self._image_col_name = None
  
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 1 to create a new ImagePredictorModel object from inherited base classes: MultiModalPredictorModel with example: obj = ImagePredictorModel()