code: def generate_dec_layer():
      hidden_size = 1024
      intermediate_size = 1024 * 4
      heads = 16
      hidden_dropout_ratio = 0.0
      attn_dropout_ratio = 0.0
      activation_dropout_ratio = 0.0
      pre_layer_norm = True
      config = TransformerDecoderLayer.get_config(
          hidden_size=hidden_size,
          intermediate_size=intermediate_size,
          nhead=heads,
          attn_prob_dropout_ratio=attn_dropout_ratio,
          activation_dropout_ratio=activation_dropout_ratio,
          hidden_dropout_ratio=hidden_dropout_ratio,
          pre_layer_norm=pre_layer_norm,
          activation_fn="relu",
          max_batch_tokens=None,
          max_seq_len=None,
          fp16=None,
          local_rank=None,
          nlayer=None,
      )
      layer = TransformerDecoderLayer(config)
  
      layer.to(torch.device("cuda:0"), dtype=torch.half)
      return layer
  
file_path: lightseq/lightseq/csrc/tests/cuda/fairseq_layers.py
imports: import sys, os
  import __init__
  from typing import Dict, List, Optional, Callable
  import math
  from copy import deepcopy
  import torch
  import torch.nn
  import torch.nn.functional
  from fairseq import utils
  from fairseq.modules import LayerNorm, MultiheadAttention
  from fairseq.modules.fairseq_dropout import FairseqDropout
  from fairseq.modules.quant_noise import quant_noise
  from torch import Tensor
  from csrc.pytorch import TransformerEncoderLayer, TransformerDecoderLayer
label_desc: Use image node_id 2 for calling a global function with example usage: generate_dec_layer() and returns: layer