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: Initialize internal variables.
docstr_tok: ["Initialize","internal","variables","."]
code: def __init__(self, model_name="efficientnet-b0", batch_size=1):
      """Initialize internal variables."""
      self.model_name = model_name
      self.batch_size = batch_size
      self.num_classes = 1000
      # Model Scaling parameters
      (
          _,
          _,
          self.image_size,
          _,
      ) = efficientnet_builder.efficientnet_params(model_name)
  
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 1 to create a new EvalCkptDriver object from inherited base classes: object with example: obj = EvalCkptDriver(model_name, batch_size)