class_docstr: Support TIMM image backbones.
  Refer to https://github.com/rwightman/pytorch-image-models
code: def get_output_dict(
      self,
      features: torch.Tensor,
      logits: torch.Tensor,
      column_features: Optional[Dict[str, torch.Tensor]] = None,
      column_feature_masks: Optional[Dict[str, torch.Tensor]] = None,
  ):
      ret = {COLUMN_FEATURES: {FEATURES: {}, MASKS: {}}}
  
      if column_features != None:
          ret[COLUMN_FEATURES][FEATURES].update(column_features)
          ret[COLUMN_FEATURES][MASKS].update(column_feature_masks)
  
      ret[FEATURES] = features
      if self.num_classes > 0:
          ret[LOGITS] = logits
  
      return {self.prefix: ret}
  
imports: import json
  import logging
  import os
  from typing import Dict, List, Optional
  import torch
  from timm import create_model
  from timm.layers.linear import Linear
  from torch import nn
  from ..constants import AUTOMM, COLUMN, COLUMN_FEATURES, FEATURES, IMAGE, IMAGE_VALID_NUM, LABEL, LOGITS, MASKS
  from .utils import assign_layer_ids, get_column_features, get_model_head
label_desc: Use image node_id 10 for calling the TimmAutoModelForImagePrediction obj's underlying member method code with example usage: obj.get_output_dict(features, logits, column_features, column_feature_masks) and returns: dict