code: def make_combined_transformation(
      tran_fns, name_prefix=None, *args, **kwargs
  ):
      """Returns a dataset transformation function that applies
      transformations to each component of the data.
  
      The data to be transformed must be a tuple of the same length
      of :attr:`tran_fns`.
  
      Args:
          tran_fns (list): A list of elements where each element is a
              transformation function or a list of transformation functions.
          name_prefix (list, optional): Prefix to the field names of each
              component of the data, to prevent fields with the same name
              in different components from overriding each other. If not `None`,
              must be of the same length of :attr:`tran_fns`.
          *args: Extra arguments for each of the transformation function.
          **kwargs: Extra keyword arguments for each of the transformation
              function.
  
      Returns:
          A transformation function to be used in
          :tf_main:`tf.data.Dataset.map <data/Dataset#map>`.
      """
      if name_prefix and len(name_prefix) != len(tran_fns):
          raise ValueError(
              "`name_prefix`, if provided, must be of the same "
              "length of `tran_fns`."
          )
  
      def _combined_fn(data):
          transformed_data = {}
          for i, tran_fns_i in enumerate(tran_fns):
              data_i = data[i]
              # Process data_i
              if not isinstance(tran_fns_i, (list, tuple)):
                  tran_fns_i = [tran_fns_i]
              for tran_fns_ij in tran_fns_i:
                  data_i = tran_fns_ij(data_i, *args, **kwargs)
              # Add to dict by appending name prefix
              for name, value in six.iteritems(data_i):
                  new_name = name
                  if name_prefix:
                      new_name = _connect_name(name_prefix[i], name)
                  if new_name in transformed_data:
                      raise ValueError(
                          "Field name already exists: {}".format(
                              new_name
                          )
                      )
                  transformed_data[new_name] = value
          return transformed_data
  
      return _combined_fn
  
imports: import six
  import tensorflow
  import numpy
  from texar.tf.utils import utils
label_desc: Use image node_id 9 for calling a global function with example usage: make_combined_transformation(tran_fns, name_prefix) and returns: _combined_fn, transformed_data