code: def test_encoder_layer_forward():
      batch_size, seq_len = kt.bs_sl()
      print(f"(batch_size, seq_len): ({batch_size}, {seq_len})")
  
      hidden_states = kt.rand((batch_size, seq_len, 1024))
      self_attn_padding_mask = kt.attn_mask(
          batch_size, seq_len, dtype=torch.bool
      )
  
      def custom():
          res = hidden_states.clone()
          for i in range(NUM_LAYERS):
              res = custom_enc_layers[i](res, self_attn_padding_mask)
          return [
              res.contiguous().detach(),
          ]
  
      def baseline():
          res = hidden_states.clone()
          for i in range(NUM_LAYERS):
              res = base_enc_layers[i](res, self_attn_padding_mask)
          return [
              res.contiguous().detach(),
          ]
  
      return custom, baseline
  
file_path: lightseq/tests/test_ls_layers_new.py
imports: import random
  import math
  from copy import deepcopy
  from dataclasses import dataclass
  import torch
  import torch.nn
  from tests.util import TestDecorator, get_fairseq_enc_params, get_fairseq_dec_params, max_batch_tokens, max_seq_len, split_custom_layer_grad, copy_grad_from_paras
  from tests import fairseq_layers
  from lightseq.training.ops.pytorch.transformer_encoder_layer_new import LSTransformerEncoderLayerNew
  from lightseq.training.ops.pytorch.transformer_encoder_layer import LSTransformerEncoderLayer
  from lightseq.training.ops.pytorch import layer_cuda_module
  from lightseq.training.cli.fs_modules.ls_fs_transformer_decoder_layer import LSFSTransformerDecoderLayerNew, LSFSTransformerDecoderLayer
label_desc: Use image node_id 4 for calling a global function with example usage: test_encoder_layer_forward() and returns: custom,  baseline, list, list