code: def test_encoder_forward_backward(
      input_layer,
      use_linear_after_conv,
      rel_pos_type,
      pos_enc_layer_type,
      attention_layer_type,
      merge_method,
      cgmlp_weight,
      attn_branch_drop_rate,
      stochastic_depth_rate,
  ):
      encoder = BranchformerEncoder(
          20,
          output_size=2,
          use_attn=True,
          attention_heads=2,
          attention_layer_type=attention_layer_type,
          pos_enc_layer_type=pos_enc_layer_type,
          rel_pos_type=rel_pos_type,
          use_cgmlp=True,
          cgmlp_linear_units=4,
          cgmlp_conv_kernel=3,
          use_linear_after_conv=use_linear_after_conv,
          gate_activation="identity",
          merge_method=merge_method,
          cgmlp_weight=cgmlp_weight,
          attn_branch_drop_rate=attn_branch_drop_rate,
          num_blocks=2,
          input_layer=input_layer,
          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])
      y, _, _ = encoder(x, x_lens)
      y.sum().backward()
  
file_path: espnet/test/espnet2/asr/encoder/test_branchformer_encoder.py
imports: import pytest
  import torch
  from espnet2.asr.encoder.branchformer_encoder import BranchformerEncoder
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, merge_method, cgmlp_weight, attn_branch_drop_rate, stochastic_depth_rate) without return types