code: def test_encoder_forward_backward(
      input_layer,
      positionwise_layer_type,
      rel_pos_type,
      pos_enc_layer_type,
      selfattention_layer_type,
      interctc_layer_idx,
      interctc_use_conditioning,
      stochastic_depth_rate,
  ):
      encoder = ConformerEncoder(
          20,
          output_size=2,
          attention_heads=2,
          linear_units=4,
          num_blocks=2,
          input_layer=input_layer,
          macaron_style=False,
          rel_pos_type=rel_pos_type,
          pos_enc_layer_type=pos_enc_layer_type,
          selfattention_layer_type=selfattention_layer_type,
          activation_type="swish",
          use_cnn_module=True,
          cnn_module_kernel=3,
          positionwise_layer_type=positionwise_layer_type,
          interctc_layer_idx=interctc_layer_idx,
          interctc_use_conditioning=interctc_use_conditioning,
          stochastic_depth_rate=stochastic_depth_rate,
      )
      if input_layer == "embed":
          x = torch.randint(0, 10, [2, 32])
      else:
          x = torch.randn(2, 32, 20, requires_grad=True)
      x_lens = torch.LongTensor([32, 28])
      if len(interctc_layer_idx) > 0:
          ctc = None
          if interctc_use_conditioning:
              vocab_size = 5
              output_size = encoder.output_size()
              ctc = CTC(
                  odim=vocab_size, encoder_output_size=output_size
              )
              encoder.conditioning_layer = torch.nn.Linear(
                  vocab_size, output_size
              )
          y, _, _ = encoder(x, x_lens, ctc=ctc)
          y = y[0]
      else:
          y, _, _ = encoder(x, x_lens)
      y.sum().backward()
  
file_path: espnet/test/espnet2/asr/encoder/test_conformer_encoder.py
imports: import pytest
  import torch
  from espnet2.asr.ctc import CTC
  from espnet2.asr.encoder.conformer_encoder import ConformerEncoder
label_desc: Use image node_id 1 for calling a global function with example usage: test_encoder_forward_backward(input_layer, positionwise_layer_type, rel_pos_type, pos_enc_layer_type, attention_layer_type, interctc_layer_idx, interctc_use_conditioning, stochastic_depth_rate) without return types