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 iterate(self, X, Y, batch_size):
      while True:
          # Shuffle array if specified
          if self.shuffle:
              idxs = np.arange(0, len(X[0]))
              np.random.shuffle(idxs)
              X = [x[idxs] for x in X]
              Y = Y[idxs]
          # Split array by batch
          for batch_idx in range(0, self.Xlen, batch_size):
              batchX = [
                  x[batch_idx : batch_idx + batch_size] for x in X
              ]
              batchY = Y[batch_idx : batch_idx + batch_size]
              yield batchX, batchY
  
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 2 for calling the ArrayFlow obj's underlying member method code with example usage: obj.iterate(X, Y, batch_size) without return types