code: def _greedy_decode(self, logits, logit_len):
      hc = Tensor.zeros(
          self.prediction.rnn.layers,
          2,
          self.prediction.hidden_size,
          requires_grad=False,
      )
      labels = []
      label = Tensor.zeros(1, 1, requires_grad=False)
      mask = Tensor.zeros(1, requires_grad=False)
      for time_idx in range(logit_len):
          logit = logits[time_idx, :, :].unsqueeze(0)
          not_blank = True
          added = 0
          while not_blank and added < 30:
              if len(labels) > 0:
                  mask = (mask + 1).clip(0, 1)
                  label = (
                      Tensor(
                          [
                              [
                                  labels[-1]
                                  if labels[-1] <= 28
                                  else labels[-1] - 1
                              ]
                          ],
                          requires_grad=False,
                      )
                      + 1
                      - 1
                  )
              jhc = self._pred_joint(
                  Tensor(logit.numpy()), label, hc, mask
              )
              k = jhc[0, 0, :29].argmax(axis=0).numpy()
              not_blank = k != 28
              if not_blank:
                  labels.append(k)
                  hc = jhc[:, :, 29:] + 1 - 1
              added += 1
      return labels
  
imports: from tinygrad.tensor import Tensor
  from tinygrad.jit import TinyJit
  from tinygrad.nn import Linear, Embedding
  from tinygrad.helpers import fetch
  import numpy
  from pathlib import Path
label_desc: Use image node_id 4 for calling the RNNT obj's underlying member method code with example usage: obj._greedy_decode(logits, logit_len) and returns: labels