code: def train_factors(
      self,
      reg_X=0.0,
      reg_F=0.0,
      mod=5,
      val_len=24,
      early_stop=False,
      tenacity=3,
      ind=None,
      seed=False,
  ):
      self.D.epoch = 0
      self.D.vindex = 0
      self.D.hindex = 0
      for p in self.Xseq.parameters():
          p.requires_grad = True
  
      l_F = [0.0]
      l_X = [0.0]
      l_X_temporal = [0.0]
      iter_count = 0
      vae = float("inf")
      scount = 0
      Xbest = self.X.clone()
      Fbest = self.F.clone()
      while self.D.epoch < self.num_epochs:
          last_epoch = self.D.epoch
          last_vindex = self.D.vindex
          last_hindex = self.D.hindex
          inp, out, vindex, hindex = self.D.next_batch()
  
          step_l_F = self.step_factF_loss(
              inp, out, last_vindex, last_hindex, reg=reg_F
          )
          l_F = l_F + [step_l_F.item()]
          step_l_X = self.step_factX_loss(
              inp, out, last_vindex, last_hindex, reg=reg_X
          )
          l_X = l_X + [step_l_X.item()]
          if seed is False and iter_count % mod == 1:
              l2 = self.step_temporal_loss_X(
                  inp, last_vindex, last_hindex
              )
              l_X_temporal = l_X_temporal + [l2.item()]
          iter_count = iter_count + 1
  
          if self.D.epoch > last_epoch:
              print("Entering Epoch#{}".format(self.D.epoch))
              print("Factorization Loss F:{}".format(np.mean(l_F)))
              print("Factorization Loss X:{}".format(np.mean(l_X)))
              print("Temporal Loss X:{}".format(np.mean(l_X_temporal)))
  
              if ind is None:
                  ind = np.arange(self.Ymat.shape[0])
              else:
                  ind = ind
              inp = self.predict_global(
                  ind,
                  last_step=self.end_index - val_len,
                  future=val_len,
              )
              R = self.Ymat[
                  ind, self.end_index - val_len : self.end_index
              ]
              S = inp[:, -val_len::]
              ve = np.abs(R - S).mean() / np.abs(R).mean()
              # print("Validation Loss (Global): ", ve)
              print("Validation Loss (Global):{}".format(ve))
              if ve <= vae:
                  vae = ve
                  scount = 0
                  Xbest = self.X.clone()
                  Fbest = self.F.clone()
                  # Xseqbest = TemporalConvNet(
                  #     num_inputs=1,
                  #     num_channels=self.num_channels_X,
                  #     kernel_size=self.kernel_size,
                  #     dropout=self.dropout,
                  # )
                  # Xseqbest.load_state_dict(self.Xseq.state_dict())
                  Xseqbest = pickle.loads(pickle.dumps(self.Xseq))
              else:
                  scount += 1
                  if scount > tenacity and early_stop:
                      # print("Early Stopped")
                      print("Early Stopped")
                      self.X = Xbest
                      self.F = Fbest
                      self.Xseq = Xseqbest
                      break
  
imports: from __future__ import print_function
  import torch
  import numpy
  import pandas
  import torch.nn
  import torch.optim
  from torch.autograd import Variable
  from zoo.chronos.model.tcmf.data_loader import TCMFDataLoader
  from zoo.chronos.model.tcmf.local_model import TemporalConvNet, LocalModel
  from zoo.chronos.model.tcmf.time import TimeCovariates
  import copy
  import pickle
  import logging
label_desc: Use image node_id 13 for calling the DeepGLO obj's underlying member method code with example usage: obj.train_factors(reg_X, reg_F, mod, val_len, early_stop, tenacity, ind, seed) without return types