code: def test_Encoder_forward_backward(
      input_layer,
      positionwise_layer_type,
      interctc_layer_idx,
      interctc_use_conditioning,
  ):
      encoder = TransformerEncoder(
          20,
          output_size=40,
          input_layer=input_layer,
          positionwise_layer_type=positionwise_layer_type,
          interctc_layer_idx=interctc_layer_idx,
          interctc_use_conditioning=interctc_use_conditioning,
      )
      if input_layer == "embed":
          x = torch.randint(0, 10, [2, 10])
      else:
          x = torch.randn(2, 10, 20, requires_grad=True)
      x_lens = torch.LongTensor([10, 8])
      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()
  
imports: import pytest
  import torch
  from espnet2.asr.ctc import CTC
  from espnet2.asr.encoder.transformer_encoder import TransformerEncoder
label_desc: Use image node_id 1 for calling a global function with example usage: test_Encoder_forward_backward(input_layer, positionwise_layer_type, interctc_layer_idx, interctc_use_conditioning) without return types