code: def get_zipped_dataset_from_predictions(predictions):
      """Creates dataset from in-memory predictions."""
      targets = stack_data_given_key(predictions, "targets")
      outputs = stack_data_given_key(predictions, "outputs")
      num_videos, num_steps = targets.shape[:2]
  
      # Truncate output time-steps to match target time-steps
      outputs = outputs[:, :num_steps]
  
      targets_placeholder = tf.placeholder(targets.dtype, targets.shape)
      outputs_placeholder = tf.placeholder(outputs.dtype, outputs.shape)
      dataset = tf.data.Dataset.from_tensor_slices(
          (targets_placeholder, outputs_placeholder)
      )
      iterator = dataset.make_initializable_iterator()
      feed_dict = {
          targets_placeholder: targets,
          outputs_placeholder: outputs,
      }
      return iterator, feed_dict, num_videos
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import numpy
  import six
  import tensorflow.compat.v1
label_desc: Use image node_id 9 for calling a global function with example usage: get_zipped_dataset_from_predictions(predictions) and returns: iterator,  feed_dict,  num_videos