class_docstr: Support TIMM image backbones.
  Refer to https://github.com/rwightman/pytorch-image-models
docstr: Whether the TIMM image support images sizes that are different from the default used in the backbones
docstr_tok: ["Whether","the","TIMM","image","support","images","sizes","that","are","different","from","the","default","used","in","the","backbones"]
code: def support_variable_input_size(self):
      """Whether the TIMM image support images sizes that are different from the default used in the backbones"""
      if (
          "test_input_size" in self.config
          and self.config["test_input_size"]
          != self.config["input_size"]
      ):
          return True
      cls_name = type(self.model).__name__.lower()
      for k in SUPPORT_VARIABLE_INPUT_SIZE_TIMM_CLASSES:
          if cls_name in k:
              return True
      return False
  
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 8 for calling the TimmAutoModelForImagePrediction obj's underlying member method code with example usage: obj.support_variable_input_size() and returns: False, True, True