class_docstr: Iterator class that generates data and provides batches for training
docstr: Initialize class
  Args:
      seq_len: int, sequence length of data
      n_train: int, number of training samples
      n_test: int, number of test samples
      batch_size: int, number of samples per batch
docstr_tok: ["Initialize","class","Args",":","seq_len",":","int",",","sequence","length","of","data","n_train",":","int",",","number","of","training","samples","n_test",":","int",",","number","of","test","samples","batch_size",":","int",",","number","of","samples","per","batch"]
code: def __init__(
      self, seq_len=200, n_train=50000, n_test=1000, batch_size=32
  ):
      """
      Initialize class
      Args:
          seq_len: int, sequence length of data
          n_train: int, number of training samples
          n_test: int, number of test samples
          batch_size: int, number of samples per batch
      """
      self.seq_len = seq_len
      self.n_train = n_train
      self.n_test = n_test
  
      x_train, y_train = self.load_data(n_train)
      x_val, y_val = self.load_data(n_test)
  
      self.train = (x_train, y_train)
  
      self.test = (x_val, y_val)
  
      self.sample_count = 0
      self.batch_size = batch_size
  
imports: import numpy
label_desc: Use image node_id 1 to create a new Adding object  with example: obj = Adding(seq_len, n_train, n_test, batch_size)