code: def get_model(
      self, hparams, mode=tf_estimator.ModeKeys.TRAIN, has_input=True
  ):
      hparams.hidden_size = 8
      hparams.filter_size = 32
      hparams.num_heads = 1
      hparams.layer_prepostprocess_dropout = 0.0
      hparams.mix_with_transformer = ""
  
      p_hparams = problem_hparams.test_problem_hparams(
          VOCAB_SIZE, VOCAB_SIZE, hparams
      )
      if not has_input:
          del p_hparams.modality["inputs"]
      hparams.problems = [p_hparams]
  
      inputs = np.random.randint(
          VOCAB_SIZE, size=(BATCH_SIZE, INPUT_LENGTH, 1, 1)
      )
      targets = np.random.randint(
          VOCAB_SIZE, size=(BATCH_SIZE, TARGET_LENGTH, 1, 1)
      )
      features = {
          "targets": tf.constant(
              targets, dtype=tf.int32, name="targets"
          ),
          "target_space_id": tf.constant(1, dtype=tf.int32),
      }
      if has_input:
          features["inputs"] = tf.constant(
              inputs, dtype=tf.int32, name="inputs"
          )
  
      return (
          universal_transformer.UniversalTransformer(
              hparams, mode, p_hparams
          ),
          features,
      )
  
file_path: tensor2tensor/tensor2tensor/models/research/universal_transformer_test.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  from tensor2tensor.data_generators import problem_hparams
  from tensor2tensor.models.research import universal_transformer
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 1 for calling the UniversalTransformerTest obj's underlying member method code with example usage: obj.get_model(hparams, mode, has_input) and returns: universal_transformer,  features