class_docstr: Produces a sequence of MNIST digits with labels.
docstr: Initializes the sequence.
  
  Args:
    data: Tuple of numpy `array` instances, the first representing images and
          the second labels.
    batch_size: Integer, number of elements in each training batch.
    fake_data_size: Optional integer number of fake datapoints to generate.
docstr_tok: ["Initializes","the","sequence",".","Args",":","data",":","Tuple","of","numpy","`","array","`","instances",",","the","first","representing","images","and","the","second","labels",".","batch_size",":","Integer",",","number","of","elements","in","each","training","batch",".","fake_data_size",":","Optional","integer","number","of","fake","datapoints","to","generate","."]
code: def __init__(self, data=None, batch_size=128, fake_data_size=None):
      """Initializes the sequence.
  
      Args:
        data: Tuple of numpy `array` instances, the first representing images and
              the second labels.
        batch_size: Integer, number of elements in each training batch.
        fake_data_size: Optional integer number of fake datapoints to generate.
      """
      if data:
          images, labels = data
      else:
          images, labels = MNISTSequence.__generate_fake_data(
              num_images=fake_data_size, num_classes=NUM_CLASSES
          )
      self.images, self.labels = MNISTSequence.__preprocessing(
          images, labels
      )
      self.batch_size = batch_size
  
imports: import os
  import warnings
  from absl import app
  from absl import flags
  import matplotlib
  from matplotlib import figure
  from matplotlib.backends import backend_agg
  import numpy
  import tensorflow.compat.v2
  import tensorflow_probability
  from tensorflow_probability.python.internal import tf_keras
label_desc: Use image node_id 1 to create a new MNISTSequence object from inherited base classes: tf_keras.utils with example: obj = MNISTSequence(data, batch_size, fake_data_size)