code: def test_discrete_asr_espnet_model(input_layer_type):
      vocab_size = 5
      src_vocab_size = 4
      enc_out = 4
      frontend = Embedding(
          input_size=src_vocab_size,
          embed_dim=6,
          positional_dropout_rate=0,
      )
      encoder = EBranchformerEncoder(
          6,
          output_size=enc_out,
          linear_units=4,
          num_blocks=2,
          input_layer=input_layer_type,
      )
      decoder = TransformerDecoder(
          vocab_size, enc_out, linear_units=4, num_blocks=2
      )
      ctc = CTC(odim=vocab_size, encoder_output_size=enc_out)
  
      model = ESPnetDiscreteASRModel(
          vocab_size,
          src_vocab_size=src_vocab_size,
          token_list=["<blank>", "<unk>", "a", "i", "<eos>"],
          frontend=frontend,
          specaug=None,
          preencoder=None,
          encoder=encoder,
          postencoder=None,
          decoder=decoder,
          ctc=ctc,
      )
  
      inputs = dict(
          src_text=torch.randint(0, 3, [2, 10], dtype=torch.long),
          src_text_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),
      )
      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.discrete_asr_espnet_model import ESPnetDiscreteASRModel
  from espnet2.asr.encoder.e_branchformer_encoder import EBranchformerEncoder
  from espnet2.mt.frontend.embedding import Embedding
label_desc: Use image node_id 1 for calling a global function with example usage: test_discrete_asr_espnet_model(input_layer_type) without return types