class_docstr: Transformer Encoder 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 forward(self, src, src_mask=None, cache=None):
      residual = src
      src = self.self_attn(src, src, src, None)
      src = residual + self.dropout1(src)
  
      src, _ = self.decomp(src)
  
      residual = src
  
      src = self.linear2(
          self.dropout(self.activation(self.linear1(src)))
      )
      src = residual + self.dropout2(src)
  
      src, _ = self.decomp(src)
      return src
  
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 2 for calling the TransformerEncoderLayer obj's underlying member method code with example usage: obj.forward(src, src_mask, cache) and returns: src