code: def predict(
      self,
      ind=None,
      last_step=100,
      future=10,
      normalize=False,
      bsize=90,
  ):
      if ind is None:
          ind = np.arange(self.Ymat.shape[0])
  
      self.Xseq = self.Xseq
  
      self.Yseq.seq = self.Yseq.seq.eval()
      self.Xseq = self.Xseq.eval()
  
      rg = max(
          1
          + 2
          * (self.kernel_size - 1)
          * 2 ** (len(self.num_channels_X) - 1),
          1
          + 2
          * (self.kernel_size_Y - 1)
          * 2 ** (len(self.num_channels_Y) - 1),
      )
      covs = self.Yseq.covariates[
          :, last_step - rg : last_step + future
      ]
      # print(covs.shape)
      yc = self.predict_global(
          ind=ind,
          last_step=last_step,
          future=future,
          normalize=False,
          bsize=bsize,
      )
      if self.period is None:
          ycovs = np.zeros(shape=[yc.shape[0], 1, yc.shape[1]])
          if self.forward_cov:
              ycovs[:, 0, 0:-1] = yc[:, 1::]
          else:
              ycovs[:, 0, :] = yc
      else:
          ycovs = np.zeros(shape=[yc.shape[0], 2, yc.shape[1]])
          if self.forward_cov:
              ycovs[:, 0, 0:-1] = yc[:, 1::]
          else:
              ycovs[:, 0, :] = yc
          period = self.period
          while last_step + future - (period - 1) > last_step + 1:
              period += self.period
          # this seems like we are looking ahead, but it will not use the last coordinate,
          # which is the only new point added
          ycovs[:, 1, period - 1 : :] = self.Ymat[
              :, last_step - rg : last_step + future - (period - 1)
          ]
  
      Y = self.Yseq.predict_future(
          data_in=self.Ymat[ind, last_step - rg : last_step],
          covariates=covs,
          ycovs=ycovs,
          future=future,
          bsize=bsize,
          normalize=False,
      )
  
      if normalize:
          Y = Y - self.mini
          Y = Y * self.s[ind, None] + self.m[ind, None]
          return Y
      else:
          return Y
  
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 23 for calling the DeepGLO obj's underlying member method code with example usage: obj.predict(ind, last_step, future, normalize, bsize) and returns: Y, Y