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/tests/fairseq_layers.py
imports: 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 lightseq.training.ops.pytorch import TransformerEncoderLayer, TransformerDecoderLayer, TransformerEmbeddingLayer
label_desc: Use image node_id 2 for calling a global function with example usage: generate_dec_layer() and returns: layer