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: Build input dataset.
docstr_tok: ["Build","input","dataset","."]
code: def build_dataset(self, filenames, labels, is_training):
      """Build input dataset."""
      filenames = tf.constant(filenames)
      labels = tf.constant(labels)
      dataset = tf.data.Dataset.from_tensor_slices((filenames, labels))
  
      def _parse_function(filename, label):
          image_string = tf.read_file(filename)
          image_decoded = preprocessing.preprocess_image(
              image_string, is_training, self.image_size
          )
          image = tf.cast(image_decoded, tf.float32)
          return image, label
  
      dataset = dataset.map(_parse_function)
      dataset = dataset.batch(self.batch_size)
  
      iterator = dataset.make_one_shot_iterator()
      images, labels = iterator.get_next()
      return images, labels
  
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 4 for calling the EvalCkptDriver obj's underlying member method code with example usage: obj.build_dataset(filenames, labels, is_training) and returns: images,  labels, image,  label