code: def step_factF_loss(
      self, inp, out, last_vindex, last_hindex, reg=0.0
  ):
      Xout = self.X[:, last_hindex + 1 : last_hindex + 1 + out.size(2)]
      Fout = self.F[
          self.D.I[last_vindex : last_vindex + out.size(0)], :
      ]
      Fout = Variable(Fout, requires_grad=True)
      optim_F = optim.Adam(params=[Fout], lr=self.lr)
      out = self.temporal_to_tensor2d(out)
      Hout = torch.matmul(Fout, Xout)
      optim_F.zero_grad()
      loss = torch.mean(torch.pow(Hout - out.detach(), 2))
      l2 = torch.mean(torch.pow(Fout, 2))
      r = loss.detach() / l2.detach()
      loss = loss + r * reg * l2
      loss.backward()
      optim_F.step()
      self.F[
          self.D.I[last_vindex : last_vindex + inp.size(0)], :
      ] = Fout.detach()
      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 7 for calling the DeepGLO obj's underlying member method code with example usage: obj.step_factF_loss(inp, out, last_vindex, last_hindex, reg) and returns: loss