class_docstr: A driver for running eval inference.
  
  Attributes:
    model_name: str. Model name to eval.
    batch_size: int. Eval batch size.
    num_classes: int. Number of classes, default to 1000 for ImageNet.
    image_size: int. Input image size, determined by model name.
class_docstr_tok: ["A","driver","for","running","eval","inference",".","Attributes",":","model_name",":","str",".","Model","name","to","eval",".","batch_size",":","int",".","Eval","batch","size",".","num_classes",":","int",".","Number","of","classes",",","default","to","1000","for","ImageNet",".","image_size",":","int",".","Input","image","size",",","determined","by","model","name","."]
docstr: Restore variables from checkpoint dir.
docstr_tok: ["Restore","variables","from","checkpoint","dir","."]
code: def restore_model(self, sess, ckpt_dir):
      """Restore variables from checkpoint dir."""
      checkpoint = tf.train.latest_checkpoint(ckpt_dir)
      ema = tf.train.ExponentialMovingAverage(decay=0.9999)
      ema_vars = (
          tf.compat.v1.trainable_variables()
          + tf.compat.v1.get_collection("moving_vars")
      )
      for v in tf.compat.v1.global_variables():
          if "moving_mean" in v.name or "moving_variance" in v.name:
              ema_vars.append(v)
      ema_vars = list(set(ema_vars))
      var_dict = ema.variables_to_restore(ema_vars)
      saver = tf.compat.v1.train.Saver(var_dict, max_to_keep=1)
      saver.restore(sess, checkpoint)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import json
  import sys
  from absl import app
  from absl import flags
  import numpy
  import tensorflow
  import efficientnet_builder
  import preprocessing
label_desc: Use image node_id 2 for calling the EvalCkptDriver obj's underlying member method code with example usage: obj.restore_model(sess, ckpt_dir) without return types