class_docstr: Autoformer is the first method to achieve the series-wise connection,
  with inherent O(LlogL) complexity
class_docstr_tok: ["Autoformer","is","the","first","method","to","achieve","the","series-wise","connection",",","with","inherent","O","(","LlogL",")","complexity"]
code: def configure_optimizers(self):
      optimizer = getattr(optim, self.optim)(
          self.parameters(), lr=self.lr
      )
      if self.lr_scheduler_milestones is not None:
          scheduler = torch.optim.lr_scheduler.MultiStepLR(
              optimizer,
              gamma=0.5,
              verbose=True,
              milestones=self.lr_scheduler_milestones,
          )
          return [optimizer], [scheduler]
      else:
          return optimizer
  
file_path: BigDL/python/chronos/src/bigdl/chronos/model/autoformer/Autoformer.py
imports: import torch
  import torch.nn
  from .layers.Embed import DataEmbedding_wo_pos
  from .layers.AutoCorrelation import AutoCorrelation, AutoCorrelationLayer
  from .layers.Autoformer_EncDec import Encoder, Decoder, EncoderLayer, DecoderLayer, my_Layernorm, series_decomp
  import torch.optim
  import pytorch_lightning
  from collections import namedtuple
  from ..utils import PYTORCH_REGRESSION_LOSS_MAP
label_desc: Use image node_id 6 for calling the AutoFormer obj's underlying member method code with example usage: obj.configure_optimizers() and returns: list,  list, optimizer