class_docstr: Ideas comes from AutoFormer
  Decompose a time series into trends and seasonal
  
  Refs:  https://arxiv.org/abs/2106.13008
code: def forward(self, x):
      t_x = paddle.transpose(x, [0, 2, 1])
      mean_x = F.avg_pool1d(
          t_x,
          self.kernel_size,
          stride=1,
          padding="SAME",
          exclusive=False,
      )
      mean_x = paddle.transpose(mean_x, [0, 2, 1])
      return x - mean_x, mean_x
  
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 SeriesDecomp obj's underlying member method code with example usage: obj.forward(x) and returns: unknown,  mean_x