code: def flatten_zip_dataset(*args):
      """A list of examples to a dataset containing mixed examples.
  
      Given a list of `n` dataset examples, flatten them by converting
      each element into a dataset and concatenating them to convert into a
      single dataset.
  
      Args:
        *args: A list containing one example each from `n` different datasets.
  
      Returns:
        flattened: A new dataset containing the examples from the list as part
          of a single dataset.
      """
      flattened = tf.data.Dataset.from_tensors(args[0])
      for ex in args[1:]:
          flattened = flattened.concatenate(
              tf.data.Dataset.from_tensors(ex)
          )
      return flattened
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from tensor2tensor.data_generators import problem
  from tensor2tensor.data_generators import text_problems
  from tensor2tensor.layers import common_layers
  from tensor2tensor.layers import discretization
  from tensor2tensor.layers import modalities
  from tensor2tensor.utils import metrics
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 2 for calling a global function with example usage: flatten_zip_dataset() and returns: flattened