code: def test_encoder_forward_backward(
      input_layer,
      use_linear_after_conv,
      rel_pos_type,
      pos_enc_layer_type,
      attention_layer_type,
      max_pos_emb_len,
      use_ffn,
      macaron_ffn,
      linear_units,
      merge_conv_kernel,
      layer_drop_rate,
      interctc_layer_idx,
      interctc_use_conditioning,
  ):
      encoder = EBranchformerEncoder(
          20,
          output_size=2,
          attention_heads=2,
          attention_layer_type=attention_layer_type,
          pos_enc_layer_type=pos_enc_layer_type,
          rel_pos_type=rel_pos_type,
          cgmlp_linear_units=4,
          cgmlp_conv_kernel=3,
          use_linear_after_conv=use_linear_after_conv,
          gate_activation="identity",
          num_blocks=2,
          input_layer=input_layer,
          max_pos_emb_len=max_pos_emb_len,
          use_ffn=use_ffn,
          macaron_ffn=macaron_ffn,
          linear_units=linear_units,
          merge_conv_kernel=merge_conv_kernel,
          layer_drop_rate=layer_drop_rate,
          interctc_layer_idx=interctc_layer_idx,
          interctc_use_conditioning=interctc_use_conditioning,
      )
      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:  # intermediate CTC
          encoder.conditioning_layer = torch.nn.Linear(2, 2)
          y, _, _ = encoder(
              x, x_lens, ctc=CTC(odim=2, encoder_output_size=2)
          )
          y, intermediate_outs = y
      else:
          y, _, _ = encoder(x, x_lens)
  
      y.sum().backward()
  
imports: import pytest
  import torch
  from espnet2.asr.ctc import CTC
  from espnet2.asr.encoder.e_branchformer_encoder import EBranchformerEncoder
label_desc: Use image node_id 1 for calling a global function with example usage: test_encoder_forward_backward(input_layer, use_linear_after_conv, rel_pos_type, pos_enc_layer_type, attention_layer_type, max_pos_emb_len, use_ffn, macaron_ffn, linear_units, merge_conv_kernel, layer_drop_rate, interctc_layer_idx, interctc_use_conditioning) without return types