code: def predict_future_batch(self, model, inp, future=10):
      out = model(inp)
      output = out[:, :, out.size(2) - 1].view(
          out.size(0), out.size(1), 1
      )
      out = torch.cat((inp, output), dim=2)
      for i in range(future - 1):
          inp = out
          out = model(inp)
          output = out[:, :, out.size(2) - 1].view(
              out.size(0), out.size(1), 1
          )
          out = torch.cat((inp, output), dim=2)
  
      out = self.temporal_to_tensor2d(out)
      out = np.array(out.detach())
      return 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 9 for calling the DeepGLO obj's underlying member method code with example usage: obj.predict_future_batch(model, inp, future) and returns: out