code: def small_librispeech_model(param_overrides=None):
      hparams = transformer.transformer_small()
      hparams.hidden_size = 8
      hparams.filter_size = 32
      hparams.num_heads = 1
      hparams.layer_prepostprocess_dropout = 0.0
      p_hparams = librispeech.Librispeech().get_hparams(hparams)
      p_hparams.vocab_size["targets"] = VOCAB_SIZE
      hparams.problem_hparams = p_hparams
      model = transformer.Transformer(
          hparams, problem_hparams=p_hparams
      )
      if param_overrides is not None:  # Add or Set any provided HParams
          assert isinstance(param_overrides, dict)
          for param_name in param_overrides:
              if hasattr(hparams, param_name):
                  hparams.set_hparam(
                      param_name, param_overrides[param_name]
                  )
              else:
                  hparams.add_hparam(
                      param_name, param_overrides[param_name]
                  )
      inputs = np.random.rand(BATCH_SIZE, INPUT_LENGTH, 80, 3).astype(
          "float32"
      )  # modify for speech
      targets = np.random.randint(
          VOCAB_SIZE, size=(BATCH_SIZE, TARGET_LENGTH, 1, 1)
      )
      features = {
          "inputs": tf.constant(
              inputs, dtype=tf.float32, name="inputs"
          ),
          "targets": tf.constant(
              targets, dtype=tf.int32, name="targets"
          ),
          "target_space_id": tf.constant(1, dtype=tf.int32),
      }
      return model, features
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import numpy
  from tensor2tensor.data_generators import librispeech
  from tensor2tensor.data_generators import problem_hparams
  from tensor2tensor.models import transformer
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 2 for calling a global function with example usage: small_librispeech_model(param_overrides) and returns: model,  features