code: def ptb_producer(raw_data, batch_size, num_steps, name=None):
    
    with tf.name_scope(name, "PTBProducer", [raw_data, batch_size, num_steps]):
      raw_data = tf.convert_to_tensor(raw_data, name="raw_data", dtype=tf.int32)
  
      data_len = tf.size(raw_data)
      batch_len = data_len // batch_size
      data = tf.reshape(raw_data[0 : batch_size * batch_len],
                        [batch_size, batch_len])
  
      epoch_size = (batch_len - 1) // num_steps
      assertion = tf.assert_positive(
          epoch_size,
          message="epoch_size == 0, decrease batch_size or num_steps")
      with tf.control_dependencies([assertion]):
        epoch_size = tf.identity(epoch_size, name="epoch_size")
  
      i = tf.train.range_input_producer(epoch_size, shuffle=False).dequeue()
      x = tf.strided_slice(data, [0, i * num_steps],
                           [batch_size, (i + 1) * num_steps])
      x.set_shape([batch_size, num_steps])
      y = tf.strided_slice(data, [0, i * num_steps + 1],
                           [batch_size, (i + 1) * num_steps + 1])
      y.set_shape([batch_size, num_steps])
      return x, y
docs: Iterate on the raw PTB data.
  
    This chunks up raw_data into batches of examples and returns Tensors that
    are drawn from these batches.
  
    Args:
      raw_data: one of the raw data outputs from ptb_raw_data.
      batch_size: int, the batch size.
      num_steps: int, the number of unrolls.
      name: the name of this operation (optional).
  
    Returns:
      A pair of Tensors, each shaped [batch_size, num_steps]. The second element
      of the tuple is the same data time-shifted to the right by one.
  
    Raises:
      tf.errors.InvalidArgumentError: if batch_size or num_steps are too high.