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","."]
docstr: Initialize internal variables.
docstr_tok: ["Initialize","internal","variables","."]
code: def __init__(
      self,
      model_name,
      batch_size=1,
      image_size=224,
      num_classes=1000,
      include_background_label=False,
  ):
      """Initialize internal variables."""
      self.model_name = model_name
      self.batch_size = batch_size
      self.num_classes = num_classes
      self.include_background_label = include_background_label
      self.image_size = image_size
  
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 1 to create a new EvalCkptDriver object from inherited base classes: object with example: obj = EvalCkptDriver(model_name, batch_size, image_size, num_classes, include_background_label)