code: def test_asr_multi_adapter_forward(self, rnnt_model, name1, name2):
      rnnt_model.eval()
      torch.random.manual_seed(0)
      input_signal = torch.randn(2, 512)
      input_signal_length = torch.tensor([512, 512], dtype=torch.int32)
  
      origial_output = rnnt_model(
          input_signal=input_signal,
          input_signal_length=input_signal_length,
      )[0]
  
      rnnt_model.add_adapter(name=name1, cfg=get_adapter_cfg())
      rnnt_model.add_adapter(name=name2, cfg=get_adapter_cfg())
      new_output = rnnt_model(
          input_signal=input_signal,
          input_signal_length=input_signal_length,
      )[0]
  
      resolved_name1 = rnnt_model.resolve_adapter_module_name_(name1)[
          -1
      ]
      resolved_name2 = rnnt_model.resolve_adapter_module_name_(name2)[
          -1
      ]
      assert rnnt_model.get_enabled_adapters() == [
          resolved_name1,
          resolved_name2,
      ]
      assert torch.mean(torch.abs(origial_output - new_output)) < 1e-5
  
imports: import pytest
  import torch
  from omegaconf import DictConfig, ListConfig, OmegaConf
  from nemo.collections.asr.models import ASRModel, EncDecCTCModel, EncDecRNNTModel
  from nemo.collections.asr.parts.submodules.adapters import multi_head_attention_adapter_module
  from nemo.collections.asr.parts.utils import adapter_utils
  from nemo.collections.common.parts import adapter_modules
  from nemo.core.classes.mixins.access_mixins import AccessMixin
  from nemo.core.classes.mixins.adapter_mixins import AdapterModuleMixin, get_registered_adapter
  from nemo.core.utils import numba_utils
  from nemo.core.utils.numba_utils import __NUMBA_MINIMUM_VERSION__
  from nemo.utils import config_utils, model_utils
label_desc: Use image node_id 16 for calling the TestASRAdapterMixin obj's underlying member method code with example usage: obj.test_asr_multi_adapter_forward(rnnt_model, name1, name2) without return types