code: def test_encoder_forward_backward(
      input_layer,
      positionwise_layer_type,
      rel_pos_type,
      pos_enc_layer_type,
      selfattention_layer_type,
  ):
      encoder = ConformerPostEncoder(
          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,
      )
      x = torch.randn(2, 32, 2, requires_grad=True)
      x_lens = torch.LongTensor([32, 28])
      print(x.shape)
      print(x_lens.shape)
      y, _ = encoder(x, x_lens)
      y.sum().backward()
  
imports: import pytest
  import torch
  from espnet2.slu.postencoder.conformer_postencoder import ConformerPostEncoder
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) without return types