def METHOD_NAME(self):
    """  Test correct bias using mnist model and data """
    def modified_parse(serialized_example):
        """
        Modified mnist dataset parser to provide only image data, without labels
        :param serialized_example:
        :return: Input image and labels
        """
        dim = 28
        features = tf.compat.v1.parse_single_example(serialized_example,
                                                     features={'label': tf.compat.v1.FixedLenFeature([], tf.int64),
                                                     'image_raw': tf.compat.v1.FixedLenFeature([], tf.string)})
        # Mnist examples are flattened. Since we aren't performing an augmentations
        # these can remain flattened.
        image = tf.compat.v1.decode_raw(features['image_raw'], tf.uint8)
        image.set_shape([dim*dim])
        # Convert from bytes to floats 0 -> 1.
        image = tf.cast(image, tf.float32) / 255
        return image
    tf.compat.v1.reset_default_graph()
    batch_size = 2
    num_samples = 10
    dataset = tf.data.TFRecordDataset([os.path.join(mnist_tfrecords_path, 'validation.tfrecords')]).repeat(1)
    dataset = dataset.map(modified_parse, num_parallel_calls=batch_size)
    dataset = dataset.batch(batch_size=batch_size)
    quant_params = QuantParams()
    bias_correction_params = BiasCorrectionParams(batch_size=batch_size,
                                                  num_quant_samples=num_samples,
                                                  num_bias_correct_samples=num_samples,
                                                  input_op_names=['reshape_input'],
                                                  output_op_names=['dense_1/BiasAdd'])
    meta_path = os.path.join(mnist_model_path, 'mnist_save.meta')
    checkpoint_path = os.path.join(mnist_model_path, 'mnist_save')
    sess = load_model_from_meta(meta_path=meta_path, checkpoint_path=checkpoint_path)
    BiasCorrection.correct_bias(sess, bias_correction_params, quant_params, dataset)
    self.assertTrue(1)      # Add some actual error check
    sess.close()