docstr: Wrapper of autogluon.multimodal.MultiModalPredictor.
  
  The features can be a mix of
  - categorical column
  - numerical column
  
  The labels can be categorical or numerical.
  
  Parameters
  ----------
  path
      The directory to store the modeling outputs.
  name
      Name of subdirectory inside path where model will be saved.
  problem_type
      Type of problem that this model will handle.
      Valid options: ['binary', 'multiclass', 'regression'].
  eval_metric
      The evaluation metric.
  num_classes
      The number of classes.
  stopping_metric
      The stopping metric.
  model
      The internal model object.
  hyperparameters
      The hyperparameters of the model
  features
      Names of the features.
  feature_metadata
      The feature metadata.
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code: def __init__(self, **kwargs):
      """Wrapper of autogluon.multimodal.MultiModalPredictor.
  
      The features can be a mix of
      - categorical column
      - numerical column
  
      The labels can be categorical or numerical.
  
      Parameters
      ----------
      path
          The directory to store the modeling outputs.
      name
          Name of subdirectory inside path where model will be saved.
      problem_type
          Type of problem that this model will handle.
          Valid options: ['binary', 'multiclass', 'regression'].
      eval_metric
          The evaluation metric.
      num_classes
          The number of classes.
      stopping_metric
          The stopping metric.
      model
          The internal model object.
      hyperparameters
          The hyperparameters of the model
      features
          Names of the features.
      feature_metadata
          The feature metadata.
      """
      super().__init__(**kwargs)
  
file_path: autogluon/tabular/src/autogluon/tabular/models/automm/ft_transformer.py
imports: import logging
  from typing import Dict
  from autogluon.common.features.types import R_CATEGORY, R_FLOAT, R_INT, S_TEXT_NGRAM, S_TEXT_SPECIAL
  from .automm_model import MultiModalPredictorModel
label_desc: Use image node_id 1 to create a new FTTransformerModel object from inherited base classes: MultiModalPredictorModel with example: obj = FTTransformerModel()