code: def train_Xseq(
      self,
      Ymat,
      num_epochs=20,
      val_len=24,
      early_stop=False,
      tenacity=3,
  ):
      seq = self.Xseq
      num_channels = self.num_channels_X
      kernel_size = self.kernel_size
      vbsize = min(self.vbsize, Ymat.shape[0] / 2)
  
      for p in seq.parameters():
          p.requires_grad = True
  
      TC = LocalModel(
          Ymat=Ymat,
          num_inputs=1,
          num_channels=num_channels,
          kernel_size=kernel_size,
          vbsize=vbsize,
          hbsize=self.hbsize,
          normalize=False,
          end_index=self.end_index - val_len,
          val_len=val_len,
          lr=self.lr,
      )
  
      TC.train_model(
          num_epochs=num_epochs,
          early_stop=early_stop,
          tenacity=tenacity,
      )
  
      self.Xseq = TC.seq
  
imports: from __future__ import print_function
  import torch
  import numpy
  import pandas
  import torch.nn
  import torch.optim
  from torch.autograd import Variable
  from bigdl.chronos.model.tcmf.data_loader import TCMFDataLoader
  from bigdl.chronos.model.tcmf.local_model import TemporalConvNet, LocalModel
  from bigdl.chronos.model.tcmf.time import TimeCovariates
  import copy
  import pickle
  import logging
label_desc: Use image node_id 12 for calling the DeepGLO obj's underlying member method code with example usage: obj.train_Xseq(Ymat, num_epochs, val_len, early_stop, tenacity) without return types