class_docstr: A driver for running eval inference.
  
  Attributes:
    model_name: str. Model name to eval.
    batch_size: int. Eval batch size.
    image_size: int. Input image size, determined by model name.
    num_classes: int. Number of classes, default to 1000 for ImageNet.
    include_background_label: whether to include extra background label.
class_docstr_tok: ["A","driver","for","running","eval","inference",".","Attributes",":","model_name",":","str",".","Model","name","to","eval",".","batch_size",":","int",".","Eval","batch","size",".","image_size",":","int",".","Input","image","size",",","determined","by","model","name",".","num_classes",":","int",".","Number","of","classes",",","default","to","1000","for","ImageNet",".","include_background_label",":","whether","to","include","extra","background","label","."]
code: def get_preprocess_fn(self):
      raise ValueError("Must be implemented by subclsses.")
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import json
  import os
  import sys
  from absl import logging
  import numpy
  import tensorflow.compat.v1
  from tensorflow.python.tpu import tpu_function
label_desc: Use image node_id 4 for calling the EvalCkptDriver obj's underlying member method code with example usage: obj.get_preprocess_fn() without return types