code: def test(self, data_path):
      current_step = 0
      num_correct = 0.0
      num_total = 0.0
  
      s_gen = DataGen(
          data_path,
          self.buckets,
          epochs=1,
          max_width=self.max_original_width,
      )
      for batch in s_gen.gen(1):
          current_step += 1
          # Get a batch (one image) and make a step.
          start_time = time.time()
          result = self.step(batch, self.forward_only)
          curr_step_time = time.time() - start_time
  
          num_total += 1
  
          output = result["prediction"]
          ground = batch["labels"][0]
          comment = batch["comments"][0]
          if sys.version_info >= (3,):
              output = output.decode("iso-8859-1")
              ground = ground.decode("iso-8859-1")
              comment = comment.decode("iso-8859-1")
  
          probability = result["probability"]
  
          if self.use_distance:
              incorrect = distance.levenshtein(output, ground)
              if not ground:
                  if not output:
                      incorrect = 0
                  else:
                      incorrect = 1
              else:
                  incorrect = float(incorrect) / len(ground)
              incorrect = min(1, incorrect)
          else:
              incorrect = 0 if output == ground else 1
  
          num_correct += 1.0 - incorrect
  
          if self.visualize:
              # Attention visualization.
              threshold = 0.5
              normalize = True
              binarize = True
              attns_list = [
                  [a.tolist() for a in step_attn]
                  for step_attn in result["attentions"]
              ]
              attns = np.array(attns_list).transpose([1, 0, 2])
              visualize_attention(
                  batch["data"],
                  "out",
                  attns,
                  output,
                  self.max_width,
                  DataGen.IMAGE_HEIGHT,
                  threshold=threshold,
                  normalize=normalize,
                  binarize=binarize,
                  ground=ground,
                  flag=None,
              )
  
          step_accuracy = "{:>4.0%}".format(1.0 - incorrect)
          if incorrect:
              correctness = step_accuracy + " ({} vs {}) {}".format(
                  output, ground, comment
              )
          else:
              correctness = step_accuracy + " (" + ground + ")"
  
          logging.info(
              "Step {:.0f} ({:.3f}s). "
              "Accuracy: {:6.2%}, "
              "loss: {:f}, perplexity: {:0<7.6}, probability: {:6.2%} {}".format(
                  current_step,
                  curr_step_time,
                  num_correct / num_total,
                  result["loss"],
                  math.exp(result["loss"])
                  if result["loss"] < 300
                  else float("inf"),
                  probability,
                  correctness,
              )
          )
  
imports: from __future__ import absolute_import
  from __future__ import division
  import time
  import os
  import math
  import logging
  import sys
  import distance
  import numpy
  import tensorflow
  from six.moves import xrange
  from .cnn import CNN
  from .seq2seq_model import Seq2SeqModel
  from ..util.data_gen import DataGen
  from ..util.visualizations import visualize_attention
label_desc: Use image node_id 3 for calling the Model obj's underlying member method code with example usage: obj.test(data_path) without return types