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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def preprocess_example_common(example, mode, hparams):
 """Preprocessing steps common to all models."""

  if "inputs" in example and hparams.max_input_seq_length > 0:
    example["inputs"] = example["inputs"][:hparams.max_input_seq_length]
  if hparams.prepend_mode != "none":
    if mode == tf.estimator.ModeKeys.PREDICT:
      example["partial_targets"] = tf.concat([example["inputs"], [0]], 0)
    else:
      example["targets"] = tf.concat(
          [example["inputs"], [0], example["targets"]], 0)
  if "targets" in example and hparams.max_target_seq_length > 0:
    example["targets"] = example["targets"][:hparams.max_target_seq_length]
  if hparams.split_to_length:
    new_example = {}
    for k, v in six.iteritems(example):
      if k == "targets" or k == "inputs":
        new_example[k] = tf.reshape(v, [-1, hparams.split_to_length, 1, 1])
      else:
        tf.logging.warning("Dropping feature %s" % k)
    return tf.data.Dataset.from_tensor_slices(new_example)
  return example