code: def gen_enc_layer_pair():
      fairseq_enc_layer = fairseq_layers.generate_enc_layer()
      fairseq_enc_layer.train()
      initial_enc_weights, initial_enc_biases = get_fairseq_enc_params(
          fairseq_enc_layer
      )
  
      custom_enc_layer_base = generate_enc_layer(
          initial_enc_weights, initial_enc_biases
      )
      custom_enc_layer_base.train()
  
      custom_enc_layer_new = generate_enc_layer_new(
          initial_enc_weights, initial_enc_biases
      )
      custom_enc_layer_new.train()
  
      return custom_enc_layer_base, custom_enc_layer_new
  
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 3 for calling a global function with example usage: gen_enc_layer_pair() and returns: custom_enc_layer_base,  custom_enc_layer_new