code: def test_RNNTDecoder(self):
      vocab = list(range(10))
      vocab = [str(x) for x in vocab]
      vocab_size = len(vocab)
  
      pred_config = OmegaConf.create(
          {
              "_target_": "nemo.collections.asr.modules.RNNTDecoder",
              "prednet": {
                  "pred_hidden": 32,
                  "pred_rnn_layers": 1,
              },
              "vocab_size": vocab_size,
              "blank_as_pad": True,
          }
      )
  
      prednet = modules.RNNTDecoder.from_config_dict(pred_config)
  
      # num params
      pred_hidden = pred_config.prednet.pred_hidden
      embed = (vocab_size + 1) * pred_hidden  # embedding with blank
      rnn = (
          2 * 4 * (pred_hidden * pred_hidden + pred_hidden)
      )  # (ih + hh) * (ifco gates) * (indim * hiddendim + bias)
      assert prednet.num_weights == (embed + rnn)
  
      # State initialization
      x_ = torch.zeros(4, dtype=torch.float32)
      states = prednet.initialize_state(x_)
  
      for state_i in states:
          assert state_i.dtype == x_.dtype
          assert state_i.device == x_.device
          assert state_i.shape[1] == len(x_)
  
      # Blank hypotheses test
      blank = vocab_size
      hyp = Hypothesis(score=0.0, y_sequence=[blank])
      cache = {}
      pred, states, _ = prednet.score_hypothesis(hyp, cache)
  
      assert pred.shape == torch.Size([1, 1, pred_hidden])
      assert len(states) == 2
      for state_i in states:
          assert state_i.dtype == pred.dtype
          assert state_i.device == pred.device
          assert state_i.shape[1] == len(pred)
  
      # Blank stateless predict
      g, states = prednet.predict(
          y=None, state=None, add_sos=False, batch_size=1
      )
  
      assert g.shape == torch.Size([1, 1, pred_hidden])
      assert len(states) == 2
      for state_i in states:
          assert state_i.dtype == g.dtype
          assert state_i.device == g.device
          assert state_i.shape[1] == len(g)
  
      # Blank stateful predict
      g, states2 = prednet.predict(
          y=None, state=states, add_sos=False, batch_size=1
      )
  
      assert g.shape == torch.Size([1, 1, pred_hidden])
      assert len(states2) == 2
      for state_i, state_j in zip(states, states2):
          assert (state_i - state_j).square().sum().sqrt() > 0.0
  
      # Predict with token and state
      token = torch.full([1, 1], fill_value=0, dtype=torch.long)
      g, states = prednet.predict(
          y=token, state=states2, add_sos=False, batch_size=None
      )
  
      assert g.shape == torch.Size([1, 1, pred_hidden])
      assert len(states) == 2
  
      # Predict with blank token and no state
      token = torch.full([1, 1], fill_value=blank, dtype=torch.long)
      g, states = prednet.predict(
          y=token, state=None, add_sos=False, batch_size=None
      )
  
      assert g.shape == torch.Size([1, 1, pred_hidden])
      assert len(states) == 2
  
imports: import pytest
  import torch
  from omegaconf import OmegaConf
  from nemo.collections.asr import modules
  from nemo.collections.asr.parts.utils.rnnt_utils import Hypothesis
  from nemo.core.utils import numba_utils
  from nemo.core.utils.numba_utils import __NUMBA_MINIMUM_VERSION__
  from nemo.utils import config_utils, logging
label_desc: Use image node_id 10 for calling the TestASRModulesBasicTests obj's underlying member method code with example usage: obj.test_RNNTDecoder() without return types