class_docstr: Iterator class that generates data and provides batches for training
docstr: Generate and load the data into numpy arrays
  Args:
      num_samples: # of data in the set
  Returns:
      Tuple of two numpy arrays, first is input data sequence, second is the addition output
docstr_tok: ["Generate","and","load","the","data","into","numpy","arrays","Args",":","num_samples",":","#","of","data","in","the","set","Returns",":","Tuple","of","two","numpy","arrays",",","first","is","input","data","sequence",",","second","is","the","addition","output"]
code: def load_data(self, num_samples):
      """
      Generate and load the data into numpy arrays
      Args:
          num_samples: # of data in the set
      Returns:
          Tuple of two numpy arrays, first is input data sequence, second is the addition output
      """
      x_num = np.random.rand(num_samples, self.seq_len, 1)
      x_mask = np.zeros((num_samples, self.seq_len, 1))
      y_data = np.zeros((num_samples, 1))
      for i in range(num_samples):
          positions = np.random.choice(
              self.seq_len, size=2, replace=False
          )
          x_mask[i, positions[0], 0] = 1
          x_mask[i, positions[1], 0] = 1
          y_data[i, 0] = (
              x_num[i, positions[0], 0] + x_num[i, positions[1], 0]
          )
      x_data = np.concatenate((x_num, x_mask), axis=2)
      return x_data, y_data
  
imports: import numpy
label_desc: Use image node_id 5 for calling the Adding obj's underlying member method code with example usage: obj.load_data(num_samples) and returns: x_data,  y_data