text: import hickle as hkl
  import numpy as np
  from keras import backend as K
  from keras.preprocessing.image import Iterator
  
  # Data generator that creates sequences for input into PredNet.
  class SequenceGenerator(Iterator):
      def __init__(self, data_file, source_file, nt,
                   batch_size=8, shuffle=False, seed=None,
                   output_mode='error', sequence_start_mode='all', N_seq=None,
                   data_format=K.image_data_format()):
          self.X = np.load(data_file)  # X will be like (n_images, nb_cols, nb_rows, nb_channels)
          self.sources = np.load(source_file) # source for each image so when creating sequences can assure that consecutive frames are from same video
          self.nt = nt
          self.batch_size = batch_size
          self.data_format = data_format
          assert sequence_start_mode in {'all', 'unique'}, 'sequence_start_mode must be in {all, unique}'
          self.sequence_start_mode = sequence_start_mode
          assert output_mode in {'error', 'prediction'}, 'output_mode must be in {error, prediction}'
          self.output_mode = output_mode
  
          if self.data_format == 'channels_first':
              self.X = np.transpose(self.X, (0, 3, 1, 2))
          self.im_shape = self.X[0].shape
  
          if self.sequence_start_mode == 'all':  # allow for any possible sequence, starting from any frame
              self.possible_starts = np.array([i for i in range(self.X.shape[0] - self.nt) if self.sources[i] == self.sources[i + self.nt - 1]])
          elif self.sequence_start_mode == 'unique':  #create sequences where each unique frame is in at most one sequence
              curr_location = 0
              possible_starts = []
              while curr_location < self.X.shape[0] - self.nt + 1:
                  if self.sources[curr_location] == self.sources[curr_location + self.nt - 1]:
                      possible_starts.append(curr_location)
                      curr_location += self.nt
                  else:
                      curr_location += 1
              self.possible_starts = possible_starts
  
          if shuffle:
              self.possible_starts = np.random.permutation(self.possible_starts)
          if N_seq is not None and len(self.possible_starts) > N_seq:  # select a subset of sequences if want to
              self.possible_starts = self.possible_starts[:N_seq]
          self.N_sequences = len(self.possible_starts)
          super(SequenceGenerator, self).__init__(len(self.possible_starts), batch_size, shuffle, seed)
  
      def __getitem__(self, null):
          return self.next()
  
      def next(self):
          with self.lock:
              current_index = (self.batch_index * self.batch_size) % self.n
              index_array, current_batch_size = next(self.index_generator), self.batch_size
          batch_x = np.zeros((current_batch_size, self.nt) + self.im_shape, np.float32)
          for i, idx in enumerate(index_array):
              idx = self.possible_starts[idx]
              batch_x[i] = self.preprocess(self.X[idx:idx+self.nt])
          if self.output_mode == 'error':  # model outputs errors, so y should be zeros
              batch_y = np.zeros(current_batch_size, np.float32)
          elif self.output_mode == 'prediction':  # output actual pixels
              batch_y = batch_x
          return batch_x, batch_y
  
      def preprocess(self, X):
          return X.astype(np.float32) / 255
  
      def create_all(self):
          X_all = np.zeros((self.N_sequences, self.nt) + self.im_shape, np.float32)
          for i, idx in enumerate(self.possible_starts):
              X_all[i] = self.preprocess(self.X[idx:idx+self.nt])
          return X_all
  