code: def predict_future(self, model, inp, future=10, bsize=90):
      n = inp.size(0)
      ids = np.arange(0, n, bsize)
      ids = list(ids) + [n]
      out = self.predict_future_batch(
          model, inp[ids[0] : ids[1], :, :], future
      )
  
      for i in range(1, len(ids) - 1):
          temp = self.predict_future_batch(
              model, inp[ids[i] : ids[i + 1], :, :], future
          )
          out = np.vstack([out, temp])
  
      out = torch.from_numpy(out).float()
      return self.tensor2d_to_temporal(out)
  
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 10 for calling the DeepGLO obj's underlying member method code with example usage: obj.predict_future(model, inp, future, bsize) and returns: self