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 __init__(
      self,
      X,
      Y,
      multi_inputs=False,
      batch_size=32,
      shuffle=True,
      capacity=None,
  ):
      # Handle multiple inputs
      if not multi_inputs:
          X = [X]
      if not capacity:
          capacity = batch_size * 8
      X = [np.array(x) for x in X]
      self.X = X
      self.Xlen = len(X[0])
      Y = np.array(Y)
      self.Y = Y
      # Create X placeholders
      self.tensorX = [
          tf.placeholder(
              dtype=tf.float32,
              shape=[None] + list(utils.get_incoming_shape(x)[1:]),
          )
          for x in X
      ]
      # Create Y placeholders
      self.tensorY = tf.placeholder(
          dtype=tf.float32,
          shape=[None] + list(utils.get_incoming_shape(Y)[1:]),
      )
      # FIFO Queue for feeding data
      self.queue = tf.FIFOQueue(
          dtypes=[x.dtype for x in self.tensorX] + [self.tensorY.dtype],
          capacity=capacity,
      )
      self.enqueue_op = self.queue.enqueue(
          self.tensorX + [self.tensorY]
      )
      self.batch_size = batch_size
      self.multi_inputs = multi_inputs
      self.shuffle = shuffle
  
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 ArrayFlow object from inherited base classes: object with example: obj = ArrayFlow(X, Y, multi_inputs, batch_size, shuffle, capacity)