code: def predict_future_batch(
      data,
      covariates=None,
      ycovs=None,
      future=10,
      model=None,
  ):
      # init inp, cov, ycovs for Local model
      valid_cov = covariates is not None
      inp = LocalModel.convert_to_input(data)
      if valid_cov:
          cov = LocalModel.convert_covariates(data, covariates)
          inp = torch.cat((inp, cov[:, :, 0 : inp.size(2)]), 1)
      if ycovs is not None:
          ycovs = LocalModel.convert_ycovs(data, ycovs)
          inp = torch.cat((inp, ycovs[:, :, 0 : inp.size(2)]), 1)
  
      ci = inp.size(2)
      for i in range(future):
          out = model(inp)
          output = out[:, :, out.size(2) - 1].view(
              out.size(0), out.size(1), 1
          )
          if valid_cov:
              output = torch.cat(
                  (
                      output,
                      cov[:, :, ci].view(cov.size(0), cov.size(1), 1),
                  ),
                  1,
              )
          if ycovs is not None:
              output = torch.cat(
                  (
                      output,
                      ycovs[:, :, ci].view(
                          ycovs.size(0), ycovs.size(1), 1
                      ),
                  ),
                  1,
              )
          out = torch.cat((inp, output), dim=2)
          inp = out
          ci += 1
      out = out[:, 0, :].view(out.size(0), 1, out.size(2))
      y = LocalModel.convert_from_output(out)
  
      return y
  
imports: import pickle
  import numpy
  import torch
  import torch.nn
  import torch.optim
  from torch.autograd import Variable
  from torch.nn.utils import weight_norm
  from bigdl.chronos.model.tcmf.data_loader import TCMFDataLoader
  from bigdl.chronos.model.tcmf.time import TimeCovariates
  import logging
label_desc: Use image node_id 9 for calling the LocalModel obj's underlying member method code with example usage: obj.predict_future_batch(data, covariates, ycovs, future, model) and returns: y