class_docstr: Produces a sequence of MNIST digits with labels.
docstr: Generates fake data in the shape of the MNIST dataset for unittest.
  
  Args:
    num_images: Integer, the number of fake images to be generated.
    num_classes: Integer, the number of classes to be generate.
  Returns:
    images: Numpy `array` representing the fake image data. The
            shape of the array will be (num_images, 28, 28).
    labels: Numpy `array` of integers, where each entry will be
            assigned a unique integer.
docstr_tok: ["Generates","fake","data","in","the","shape","of","the","MNIST","dataset","for","unittest",".","Args",":","num_images",":","Integer",",","the","number","of","fake","images","to","be","generated",".","num_classes",":","Integer",",","the","number","of","classes","to","be","generate",".","Returns",":","images",":","Numpy","`","array","`","representing","the","fake","image","data",".","The","shape","of","the","array","will","be","(","num_images",",","28",",","28",")",".","labels",":","Numpy","`","array","`","of","integers",",","where","each","entry","will","be","assigned","a","unique","integer","."]
code: def __generate_fake_data(num_images, num_classes):
      """Generates fake data in the shape of the MNIST dataset for unittest.
  
      Args:
        num_images: Integer, the number of fake images to be generated.
        num_classes: Integer, the number of classes to be generate.
      Returns:
        images: Numpy `array` representing the fake image data. The
                shape of the array will be (num_images, 28, 28).
        labels: Numpy `array` of integers, where each entry will be
                assigned a unique integer.
      """
      images = np.random.randint(
          low=0,
          high=256,
          size=(num_images, IMAGE_SHAPE[0], IMAGE_SHAPE[1]),
      )
      labels = np.random.randint(
          low=0, high=num_classes, size=num_images
      )
      return images, labels
  
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 2 for calling the MNISTSequence obj's underlying member method code with example usage: obj.__generate_fake_data(num_images, num_classes) and returns: images,  labels