code: def transform_preprocess(
      labels=None, blank_index=None, num_class=None
  ):
      """Ensure that the value of blank_index is in a reasonable range,
      and transform the DenseTensor labels to a SparseTensor"""
      if blank_index is None or blank_index < 0:
          raise ValueError(
              "blank_index must be greater than or equal to zero"
          )
  
      if not num_class is None and blank_index > (num_class - 1):
          raise ValueError(
              "blank_index must be less than or equal to num_class - 1"
          )
  
      if labels is None:
          return None
  
      if not isinstance(labels, tf.SparseTensor):
          labels = tf.cast(labels, tf.int32)
          labels_idx = tf.where(tf.not_equal(labels, 0))
          labels_values = tf.gather_nd(labels, labels_idx)
          labels_shape = tf.cast(tf.shape(labels), dtype=tf.int64)
          labels = tf.SparseTensor(
              indices=labels_idx,
              values=labels_values,
              dense_shape=labels_shape,
          )
  
      return labels
  
imports: import delta.compat
label_desc: Use image node_id 1 for calling a global function with example usage: transform_preprocess(labels, blank_index, num_class) and returns: labels, None