class_docstr: Initialize the Lightseq Transformer Decoder Layer.
  
  Static variable:
      layer_id: The layer-index counter starting from 0 and incrementing by 1 every time a layer object is instantiated,
      e.g. if a model has 24 transformer layers, layer_id goes from 0 to 23.
  Arguments:
      config: An object of LSTransformerDecoderLayer config, see get_config
  
      initial_weights: Optional: Only used for unit test
  
      initial_biases: Optional: Only used for unit test
class_docstr_tok: ["Initialize","the","Lightseq","Transformer","Decoder","Layer",".","Static","variable",":","layer_id",":","The","layer-index","counter","starting","from","0","and","incrementing","by","1","every","time","a","layer","object","is","instantiated",",","e.g",".","if","a","model","has","24","transformer","layers",",","layer_id","goes","from","0","to","23",".","Arguments",":","config",":","An","object","of","LSTransformerDecoderLayer","config",",","see","get_config","initial_weights",":","Optional",":","Only","used","for","unit","test","initial_biases",":","Optional",":","Only","used","for","unit","test"]
code: def enable_quant(self):
      self.quant_mode = True
  
imports: from itertools import zip_longest
  import math
  from dataclasses import dataclass
  import copy
  import torch
  from torch import nn
  from torch.autograd import Function
  from lightseq.training.ops.pytorch.util import copy_para, state_dict, MODEL_ARCH, check_config, calc_offset
  from lightseq.training.ops.pytorch.layer_base import TransformerDecoderLayerBase
label_desc: Use image node_id 11 for calling the LSTransformerDecoderLayer obj's underlying member method code with example usage: obj.enable_quant() without return types