docs: def readMNIST(sc, output, format):
    """Reads/verifies previously created output"""
  
    output_images = output + "/images"
    output_labels = output + "/labels"
    imageRDD = None
    labelRDD = None
  
    if format == "pickle":
      imageRDD = sc.pickleFile(output_images)
      labelRDD = sc.pickleFile(output_labels)
    elif format == "csv":
      imageRDD = sc.textFile(output_images).map(fromCSV)
      labelRDD = sc.textFile(output_labels).map(fromCSV)
    else:  # format.startswith("tf"):
      # requires: --jars tensorflow-hadoop-1.0-SNAPSHOT.jar
      tfRDD = sc.newAPIHadoopFile(output, "org.tensorflow.hadoop.io.TFRecordFileInputFormat",
                                  keyClass="org.apache.hadoop.io.BytesWritable",
                                  valueClass="org.apache.hadoop.io.NullWritable")
      imageRDD = tfRDD.map(lambda x: fromTFExample(bytes(x[0])))
  
    num_images = imageRDD.count()
    num_labels = labelRDD.count() if labelRDD is not None else num_images
    samples = imageRDD.take(10)
    print("num_images: ", num_images)
    print("num_labels: ", num_labels)
    print("samples: ", samples)