class_docstr: Iterator class that generates data and provides batches for training
docstr: Return one batch at a time
  Returns:
      Tuple of two numpy arrays, first is input data sequence, second is the addition output
docstr_tok: ["Return","one","batch","at","a","time","Returns",":","Tuple","of","two","numpy","arrays",",","first","is","input","data","sequence",",","second","is","the","addition","output"]
code: def get_batch(self):
      """
      Return one batch at a time
      Returns:
          Tuple of two numpy arrays, first is input data sequence, second is the addition output
      """
      if self.sample_count + self.batch_size > self.n_train:
          self.sample_count = 0
  
      batch = (
          self.train[0][
              self.sample_count : self.sample_count + self.batch_size
          ],
          self.train[1][
              self.sample_count : self.sample_count + self.batch_size
          ],
      )
      self.sample_count += self.batch_size
      return batch
  
imports: import numpy
label_desc: Use image node_id 4 for calling the Adding obj's underlying member method code with example usage: obj.get_batch() and returns: batch