code: def squeezeformer_model():
      preprocessor = {
          "cls": "nemo.collections.asr.modules.AudioToMelSpectrogramPreprocessor",
          "params": dict({}),
      }
      encoder = {
          "cls": "nemo.collections.asr.modules.SqueezeformerEncoder",
          "params": {
              "feat_in": 80,
              "feat_out": -1,
              "n_layers": 2,
              "adaptive_scale": True,
              "time_reduce_idx": 1,
              "time_recovery_idx": None,
              "d_model": 256,
              "subsampling": "dw_striding",
              "subsampling_factor": 4,
              "subsampling_conv_channels": 512,
              "ff_expansion_factor": 4,
              "self_attention_model": "rel_pos",
              "n_heads": 8,
              "att_context_size": [-1, -1],
              "xscaling": True,
              "untie_biases": True,
              "pos_emb_max_len": 500,
              "conv_kernel_size": 31,
              "dropout": 0.1,
              "dropout_emb": 0.0,
              "dropout_att": 0.1,
          },
      }
  
      decoder = {
          "cls": "nemo.collections.asr.modules.ConvASRDecoder",
          "params": {
              "feat_in": 256,
              "num_classes": 1024,
              "vocabulary": list(chr(i % 28) for i in range(0, 1024)),
          },
      }
  
      modelConfig = DictConfig(
          {
              "preprocessor": DictConfig(preprocessor),
              "encoder": DictConfig(encoder),
              "decoder": DictConfig(decoder),
          }
      )
      conformer_model = EncDecCTCModel(cfg=modelConfig)
      return conformer_model
  
imports: import os
  import tempfile
  import onnx
  import pytest
  import torch.cuda
  from omegaconf import DictConfig, ListConfig, OmegaConf
  from nemo.collections.asr.models import EncDecClassificationModel, EncDecCTCModel, EncDecRNNTModel, EncDecSpeakerLabelModel
  from nemo.collections.asr.parts.utils import asr_module_utils
  from nemo.collections.common.parts.adapter_modules import LinearAdapterConfig
  from nemo.core.utils import numba_utils
  from nemo.core.utils.numba_utils import __NUMBA_MINIMUM_VERSION__
label_desc: Use image node_id 6 for calling a global function with example usage: squeezeformer_model() and returns: conformer_model