code: def test_forward(self, hybrid_asr_model):
      hybrid_asr_model = hybrid_asr_model.eval()
  
      hybrid_asr_model.preprocessor.featurizer.dither = 0.0
      hybrid_asr_model.preprocessor.featurizer.pad_to = 0
  
      hybrid_asr_model.compute_eval_loss = False
  
      input_signal = torch.randn(size=(4, 512))
      length = torch.randint(low=161, high=500, size=[4])
  
      with torch.no_grad():
          # batch size 1
          logprobs_instance = []
          for i in range(input_signal.size(0)):
              logprobs_ins, _ = hybrid_asr_model.forward(
                  input_signal=input_signal[i : i + 1],
                  input_signal_length=length[i : i + 1],
              )
              logprobs_instance.append(logprobs_ins)
          logits_instance = torch.cat(logprobs_instance, 0)
  
          # batch size 4
          logprobs_batch, _ = hybrid_asr_model.forward(
              input_signal=input_signal, input_signal_length=length
          )
  
      assert logits_instance.shape == logprobs_batch.shape
      diff = torch.mean(torch.abs(logits_instance - logprobs_batch))
      assert diff <= 1e-6
      diff = torch.max(torch.abs(logits_instance - logprobs_batch))
      assert diff <= 1e-6
  
imports: import os
  import shutil
  import tempfile
  import pytest
  import torch
  from omegaconf import DictConfig
  from nemo.collections.asr.models.hybrid_rnnt_ctc_bpe_models import EncDecHybridRNNTCTCBPEModel
  from nemo.collections.asr.parts.submodules import rnnt_beam_decoding
  from nemo.collections.asr.parts.submodules import rnnt_greedy_decoding
  from nemo.collections.asr.parts.submodules.ctc_decoding import CTCBPEDecoding, CTCBPEDecodingConfig
  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 2 for calling the TestEncDecHybridRNNTCTCBPEModel obj's underlying member method code with example usage: obj.test_forward(hybrid_asr_model) without return types