code: def __init__(
      self,
      vbsize=150,
      hbsize=256,
      num_channels_X=[32, 32, 32, 32, 1],
      num_channels_Y=[32, 32, 32, 32, 1],
      kernel_size=7,
      dropout=0.2,
      rank=64,
      kernel_size_Y=7,
      lr=0.0005,
      normalize=False,
      use_time=True,
      svd=False,
      forward_cov=False,
  ):
      self.use_time = use_time
      self.dropout = dropout
      self.forward_cov = forward_cov
      self.Xseq = TemporalConvNet(
          num_inputs=1,
          num_channels=num_channels_X,
          kernel_size=kernel_size,
          dropout=dropout,
          init=True,
      )
      self.vbsize = vbsize
      self.hbsize = hbsize
      self.num_channels_X = num_channels_X
      self.num_channels_Y = num_channels_Y
      self.kernel_size_Y = kernel_size_Y
      self.rank = rank
      self.kernel_size = kernel_size
      self.lr = lr
      self.normalize = normalize
      self.svd = svd
  
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 1 to create a new DeepGLO object from inherited base classes: object with example: obj = DeepGLO(vbsize, hbsize, num_channels_X, num_channels_Y, kernel_size, dropout, rank, kernel_size_Y, lr, normalize, use_time, svd, forward_cov)