class_docstr: Outputs multiple datasets.
  
  This Pipeline will take a single input feed and randomly partition the inputs
  into multiple output datasets. The probabilities of an input landing in each
  dataset are given by `partition_probabilities`. Use this Pipeline to partition
  previous Pipeline outputs into training and test sets, or training, eval, and
  test sets.
code: def transform(self, input_object):
      r = self.rand_func()
      if r >= self.cumulative_density[-1]:
          bucket = len(self.cumulative_density)
      else:
          for i, cpd in enumerate(self.cumulative_density):
              if r < cpd:
                  bucket = i
                  break
      self._set_stats(self._make_stats(self.partition_names[bucket]))
      return dict(
          (name, [] if i != bucket else [input_object])
          for i, name in enumerate(self.partition_names)
      )
  
file_path: magenta/magenta/pipelines/pipelines_common.py
imports: import numbers
  import random
  from magenta.pipelines import pipeline
  from magenta.pipelines import statistics
  import numpy
  import tensorflow.compat.v1
label_desc: Use image node_id 2 for calling the RandomPartition obj's underlying member method code with example usage: obj.transform(input_object) and returns: dict