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 _make_stats(self, increment_partition=None):
      return [statistics.Counter(increment_partition + "_count", 1)]
  
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 3 for calling the RandomPartition obj's underlying member method code with example usage: obj._make_stats(increment_partition) and returns: list