class_docstr: Support TIMM image backbones.
  Refer to https://github.com/rwightman/pytorch-image-models
code: def save(
      self, save_path: str = "./", tokenizers: Optional[dict] = None
  ):
      weights_path = f"{save_path}/pytorch_model.bin"
      torch.save(self.model.state_dict(), weights_path)
      logger.info(
          f"Model {self.prefix} weights saved to {weights_path}."
      )
      config_path = f"{save_path}/config.json"
      self.dump_config(config_path)
  
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 13 for calling the TimmAutoModelForImagePrediction obj's underlying member method code with example usage: obj.save(save_path, tokenizers) without return types