class_docstr: ArrayFlow.
  
  Convert array samples to tensors and store them in a queue.
  
  Arguments:
      X: `array`. The features data array.
      Y: `array`. The targets data array.
      multi_inputs: `bool`. Set to True if X has multiple input sources (i.e.
          X is a list of arrays).
      batch_size: `int`. The batch size.
      shuffle: `bool`. If True, data will be shuffled.
  
  Returns:
      The `X` and `Y` data tensors or a list(`X`) and `Y` data tensors if
      multi_inputs is True.
class_docstr_tok: ["ArrayFlow",".","Convert","array","samples","to","tensors","and","store","them","in","a","queue",".","Arguments",":","X",":","`","array","`",".","The","features","data","array",".","Y",":","`","array","`",".","The","targets","data","array",".","multi_inputs",":","`","bool","`",".","Set","to","True","if","X","has","multiple","input","sources","(","i.e",".","X","is","a","list","of","arrays",")",".","batch_size",":","`","int","`",".","The","batch","size",".","shuffle",":","`","bool","`",".","If","True",",","data","will","be","shuffled",".","Returns",":","The","`","X","`","and","`","Y","`","data","tensors","or","a","list","(","`","X","`",")","and","`","Y","`","data","tensors","if","multi_inputs","is","True","."]
code: def launch_threads(self, session, num_threads=1):
      threads = []
      for i in range(num_threads):
          t = threading.Thread(target=self.thread_main, args=(session,))
          t.daemon = True
          t.start()
          threads.append(t)
      return threads
  
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 4 for calling the ArrayFlow obj's underlying member method code with example usage: obj.launch_threads(session, num_threads) and returns: threads