code: def step(self, batch, forward_only):
      img_data = batch["data"]
      decoder_inputs = batch["decoder_inputs"]
      target_weights = batch["target_weights"]
  
      # Input feed: encoder inputs, decoder inputs, target_weights, as provided.
      input_feed = {}
      input_feed[self.img_pl.name] = img_data
  
      for idx in xrange(self.decoder_size):
          input_feed[self.decoder_inputs[idx].name] = decoder_inputs[
              idx
          ]
          input_feed[self.target_weights[idx].name] = target_weights[
              idx
          ]
  
      # Since our targets are decoder inputs shifted by one, we need one more.
      last_target = self.decoder_inputs[self.decoder_size].name
      input_feed[last_target] = np.zeros(
          [self.batch_size], dtype=np.int32
      )
  
      # Output feed: depends on whether we do a backward step or not.
      output_feed = [
          self.attention_decoder_model.loss,  # Loss for this batch.
      ]
  
      if not forward_only:
          output_feed += [self.summaries_by_bucket[0], self.updates[0]]
      else:
          output_feed += [self.prediction]
          output_feed += [self.probability]
          if self.visualize:
              output_feed += self.attention_decoder_model.attentions
  
      outputs = self.sess.run(output_feed, input_feed)
  
      res = {
          "loss": outputs[0],
      }
  
      if not forward_only:
          res["summaries"] = outputs[1]
      else:
          res["prediction"] = outputs[1]
          res["probability"] = outputs[2]
          if self.visualize:
              res["attentions"] = outputs[3:]
  
      return res
  
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 5 for calling the Model obj's underlying member method code with example usage: obj.step(batch, forward_only) and returns: res