code: def step_temporal_loss_X(self, inp, last_vindex, last_hindex):
      Xin = self.X[:, last_hindex : last_hindex + inp.size(2)]
      Xout = self.X[:, last_hindex + 1 : last_hindex + 1 + inp.size(2)]
      for p in self.Xseq.parameters():
          p.requires_grad = False
      Xin = Variable(Xin, requires_grad=True)
      Xout = Variable(Xout, requires_grad=True)
      optim_out = optim.Adam(params=[Xout], lr=self.lr)
      Xin = self.tensor2d_to_temporal(Xin)
      Xout = self.tensor2d_to_temporal(Xout)
      hatX = self.Xseq(Xin)
      optim_out.zero_grad()
      loss = torch.mean(torch.pow(Xout - hatX.detach(), 2))
      loss.backward()
      optim_out.step()
      # Xout = torch.clamp(Xout, min=0)
      temp = self.temporal_to_tensor2d(Xout.detach())
      self.X[:, last_hindex + 1 : last_hindex + 1 + inp.size(2)] = temp
      return loss
  
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 8 for calling the DeepGLO obj's underlying member method code with example usage: obj.step_temporal_loss_X(inp, last_vindex, last_hindex) and returns: loss