class_docstr: Transformer Decoder with Time Series Decomposition
  
  Ideas comes from AutoFormer
  
  Decoding trends and seasonal 
  Decompose a time series into trends and seasonal
  
  Refs:  https://arxiv.org/abs/2106.13008
code: def __init__(
      self,
      d_model,
      nhead,
      dims_feedforward,
      dropout=0.1,
      activation="gelu",
      attn_dropout=None,
      act_dropout=None,
      trends_out=134,
      weight_attr=None,
      bias_attr=None,
  ):
      self._config = locals()
      self._config.pop("self")
      self._config.pop("__class__", None)  # py3
  
      super(TransformerDecoderLayer, self).__init__()
  
      attn_dropout = dropout if attn_dropout is None else attn_dropout
      act_dropout = dropout if act_dropout is None else act_dropout
  
      self.decomp = SeriesDecomp(DECOMP)
  
      self.self_attn = nn.MultiHeadAttention(d_model, nhead)
  
      self.cross_attn = nn.MultiHeadAttention(d_model, nhead)
  
      self.linear1 = nn.Linear(d_model, dims_feedforward)
      self.dropout = nn.Dropout(act_dropout, mode="upscale_in_train")
      self.linear2 = nn.Linear(dims_feedforward, d_model)
  
      self.linear_trend = nn.Conv1D(
          d_model, trends_out, WIN, padding="SAME", data_format="NLC"
      )
  
      self.dropout1 = nn.Dropout(dropout, mode="upscale_in_train")
      self.dropout2 = nn.Dropout(dropout, mode="upscale_in_train")
      self.activation = getattr(F, activation)
  
imports: import pgl
  import paddle
  import paddle.nn
  import paddle.nn.functional
  import numpy
  from pgl.utils.logger import log
  import pgl.nn
  import math
label_desc: Use image node_id 1 to create a new TransformerDecoderLayer object from inherited base classes: nn with example: obj = TransformerDecoderLayer(d_model, nhead, dims_feedforward, dropout, activation, attn_dropout, act_dropout, trends_out, weight_attr, bias_attr)