class_docstr: Data Flow.
  
  Base class for using real time pre-processing and controlling data flow.
  Supports pipelining for faster computation.
  
  Arguments:
      coord: `Coordinator`. A Tensorflow coordinator.
      num_threads: `int`. Total number of simultaneous threads to process data.
      max_queue: `int`. Maximum number of data stored in a queue.
      shuffle: `bool`. If True, data will be shuffle.
      continuous: `bool`. If True, when an epoch is over, same data will be
          feeded again.
      ensure_data_order: `bool`. Ensure that data order is keeped when using
          'next' to retrieve data (Processing will be slower).
      dprep_dict: dict. Optional data pre-processing parameter for performing
          real time data pre-processing. Keys must be placeholders and values
          `DataPreprocessing` subclass object.
      daug_dict: dict. Optional data augmentation parameter for performing
          real time data augmentation. Keys must be placeholders and values
          `DataAugmentation` subclass object.
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code: def __init__(
      self,
      coord,
      num_threads=8,
      max_queue=32,
      shuffle=False,
      continuous=False,
      ensure_data_order=False,
      dprep_dict=None,
      daug_dict=None,
  ):
      self.coord = coord
      self.num_threads = num_threads
      self.max_queue = max_queue
      self.shuffle = shuffle
      self.continuous = continuous
      if ensure_data_order:
          self.num_threads = 1
          self.max_queue = 1
      self.dprep_dict = dprep_dict
      self.daug_dict = daug_dict
      self.interrupted = False
  
imports: from __future__ import division, print_function, absolute_import
  import numpy
  import threading
  import tensorflow.compat.v1
  from .None import utils
label_desc: Use image node_id 1 to create a new DataFlow object from inherited base classes: object with example: obj = DataFlow(coord, num_threads, max_queue, shuffle, continuous, ensure_data_order, dprep_dict, daug_dict)