class_docstr: A Discretized Stream (DStream), the basic abstraction in Spark Streaming,
  is a continuous sequence of RDDs (of the same type) representing a
  continuous stream of data (see :class:`RDD` in the Spark core documentation
  for more details on RDDs).
  
  DStreams can either be created from live data (such as, data from TCP
  sockets, etc.) using a :class:`StreamingContext` or it can be
  generated by transforming existing DStreams using operations such as
  `map`, `window` and `reduceByKeyAndWindow`. While a Spark Streaming
  program is running, each DStream periodically generates a RDD, either
  from live data or by transforming the RDD generated by a parent DStream.
  
  DStreams internally is characterized by a few basic properties:
   - A list of other DStreams that the DStream depends on
   - A time interval at which the DStream generates an RDD
   - A function that is used to generate an RDD after each time interval
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docstr: Print the first num elements of each RDD generated in this DStream.
  
  Parameters
  ----------
  num : int, optional
      the number of elements from the first will be printed.
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code: def pprint(self, num: int = 10) -> None:
      """
      Print the first num elements of each RDD generated in this DStream.
  
      Parameters
      ----------
      num : int, optional
          the number of elements from the first will be printed.
      """
  
      def takeAndPrint(time: datetime, rdd: RDD[T]) -> None:
          taken = rdd.take(num + 1)
          print("-------------------------------------------")
          print("Time: %s" % time)
          print("-------------------------------------------")
          for record in taken[:num]:
              print(record)
          if len(taken) > num:
              print("...")
          print("")
  
      self.foreachRDD(takeAndPrint)
  
imports: import operator
  import time
  from itertools import chain
  from datetime import datetime
  from typing import Any, Callable, Generic, Hashable, Iterable, List, Optional, Tuple, TypeVar, Union, TYPE_CHECKING, cast, overload
  from py4j.protocol import Py4JJavaError
  from py4j.java_gateway import JavaObject
  from pyspark.storagelevel import StorageLevel
  from pyspark.streaming.util import rddToFileName, TransformFunction
  from pyspark.rdd import portable_hash, RDD
  from pyspark.resultiterable import ResultIterable
label_desc: Use image node_id 16 for calling the DStream obj's underlying member method code with example usage: obj.pprint(num) without return types