code: def calculate_newX_loss_vanilla(self, Xn, Fn, Yn, Xf, alpha):
      Yout = torch.mm(Fn, Xn)
      cr1 = nn.L1Loss()
      cr2 = nn.MSELoss()
      l1 = cr2(Yout, Yn) / torch.mean(Yn**2)
      l2 = cr2(Xn, Xf) / torch.mean(Xf**2)
      return (1 - alpha) * l1 + alpha * l2
  
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 4 for calling the DeepGLO obj's underlying member method code with example usage: obj.calculate_newX_loss_vanilla(Xn, Fn, Yn, Xf, alpha) and returns: unknown