code: def test_pit_espnet_model(encoder_arch, num_inf):
      vocab_size = 5
      enc_out = 4
      encoder = encoder_arch(
          20,
          output_size=enc_out,
          linear_units=4,
          num_blocks=1,
          num_blocks_sd=1,
          num_inf=num_inf,
      )
      decoder = TransformerDecoder(
          vocab_size, enc_out, linear_units=4, num_blocks=2
      )
      ctc = CTC(
          odim=vocab_size, encoder_output_size=enc_out, reduce=False
      )
  
      model = ESPnetASRModel(
          vocab_size,
          token_list=["<blank>", "<unk>", "a", "i", "<eos>"],
          frontend=None,
          specaug=None,
          normalize=None,
          preencoder=None,
          encoder=encoder,
          postencoder=None,
          decoder=decoder,
          ctc=ctc,
          joint_network=None,
          num_inf=num_inf,
          num_ref=num_inf,
      )
  
      inputs = dict(
          speech=torch.randn(2, 10, 20, requires_grad=True),
          speech_lengths=torch.tensor([10, 8], dtype=torch.long),
          text=torch.randint(2, 4, [2, 4], dtype=torch.long),
          text_lengths=torch.tensor([4, 3], dtype=torch.long),
      )
      if num_inf > 1:
          for i in range(2, num_inf + 1):
              inputs[f"text_spk{i}"] = torch.randint(
                  2, 4, [2, 4], dtype=torch.long
              )
              inputs[f"text_spk{i}_lengths"] = torch.tensor(
                  [4, 3], dtype=torch.long
              )
  
      loss, *_ = model(**inputs)
      loss.backward()
  
imports: import pytest
  import torch
  from espnet2.asr.ctc import CTC
  from espnet2.asr.decoder.transformer_decoder import TransformerDecoder
  from espnet2.asr.encoder.transformer_encoder_multispkr import TransformerEncoder
  from espnet2.asr.pit_espnet_model import ESPnetASRModel
label_desc: Use image node_id 1 for calling a global function with example usage: test_pit_espnet_model(encoder_arch, num_inf) without return types