code: def get_model(hparams, has_input=True, num_decoder_layers=1):
      hparams.layer_prepostprocess_dropout = 0.0
      hparams.hidden_size = 4
      hparams.num_heads = 1
      hparams.num_encoder_layers = 1
      hparams.num_decoder_layers = num_decoder_layers
  
      p_hparams = problem_hparams.test_problem_hparams(
          VOCAB_SIZE, VOCAB_SIZE, hparams
      )
      if not has_input:
          del p_hparams.modality["inputs"]
      hparams.problem_hparams = 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 (
          evolved_transformer.EvolvedTransformer(
              hparams, tf_estimator.ModeKeys.TRAIN, p_hparams
          ),
          features,
      )
  
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 import evolved_transformer
  from tensor2tensor.models import transformer
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 5 for calling a global function with example usage: get_model(hparams, has_input, num_decoder_layers) and returns: evolved_transformer,  features