code: def get_mnist_random_output(
      self,
      model_name,
      hparams_set=None,
      mode=tf_estimator.ModeKeys.TRAIN,
  ):
      hparams_set = hparams_set or model_name
      x = np.random.randint(256, size=(1, 28, 28, 1))
      y = np.random.randint(10, size=(1, 1))
      features = {
          "targets": tf.constant(x, dtype=tf.int32),
          "inputs": tf.constant(y, dtype=tf.int32),
      }
      hparams = trainer_lib.create_hparams(
          hparams_set, problem_name="image_mnist_rev", data_dir="."
      )
      model = registry.model(model_name)(hparams, mode)
      tf.train.create_global_step()
      logits, _ = model(features)
      with self.test_session() as session:
          session.run(tf.global_variables_initializer())
          res = session.run(logits)
      return res
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  from tensor2tensor.data_generators import mnist
  from tensor2tensor.models.research import autoencoders
  from tensor2tensor.utils import registry
  from tensor2tensor.utils import trainer_lib
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 1 for calling the AutoencoderTest obj's underlying member method code with example usage: obj.get_mnist_random_output(model_name, hparams_set, mode) and returns: res