code: def __init__(self, cfg: DictConfig, trainer: "Trainer" = None):
      super().__init__(cfg=cfg, trainer=trainer)
  
      if "inner_model" in self.cfg:
          self.register_nemo_submodule(
              "inner_model",
              config_field="inner_model",
              model=EncDecRNNTBPEModel(self.cfg.inner_model),
          )
      else:
          # Restore a model from pretrained checkpoint
          self.register_nemo_submodule(
              "inner_model",
              config_field="inner_model",
              model=ASRModel.from_pretrained(
                  "stt_en_conformer_transducer_small",
                  map_location="cpu",
              ),
          )
  
      self.linear = torch.nn.Linear(
          self.inner_model.tokenizer.vocab_size + 1,
          self.inner_model.tokenizer.vocab_size + 1,
      )
      self.inner_model.freeze()
  
imports: import os
  import shutil
  import tempfile
  import pytest
  import torch
  from omegaconf import DictConfig
  from nemo.collections.asr.models import ASRModel
  from nemo.collections.asr.models.rnnt_bpe_models import EncDecRNNTBPEModel
  from nemo.collections.asr.parts.submodules import rnnt_beam_decoding
  from nemo.collections.asr.parts.submodules import rnnt_greedy_decoding
  from nemo.collections.common import tokenizers
  from nemo.core.utils import numba_utils
  from nemo.core.utils.numba_utils import __NUMBA_MINIMUM_VERSION__
label_desc: Use image node_id 1 to create a new NestedRNNTModel object from inherited base classes: ASRModel with example: obj = NestedRNNTModel(cfg, trainer)