class_docstr: Data Loader Class for DeepGLO
docstr: Argeuments:
  Ymat: time-series matrix n*T
  covariates: global covariates common for all time series r*T, where r is the number of
      covariates
  Ycov: per time-series covariates n*l*T, l such covariates per time-series
  All of the above arguments are numpy arrays
  vbsize: vertical batch size
  hbsize: horizontal batch size
  end_index: training and validation set is only from 0:end_index
  val_len: validation length. The last 'val_len' time-points for every time-series is the
      validation set
  shuffle: data is shuffles if True (this is deprecated and set to False)
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code: def __init__(
      self,
      Ymat,
      covariates=None,
      Ycov=None,
      vbsize=200,
      hbsize=100,
      end_index=20000,
      val_len=30,
      shuffle=False,
  ):
      """
      Argeuments:
      Ymat: time-series matrix n*T
      covariates: global covariates common for all time series r*T, where r is the number of
          covariates
      Ycov: per time-series covariates n*l*T, l such covariates per time-series
      All of the above arguments are numpy arrays
      vbsize: vertical batch size
      hbsize: horizontal batch size
      end_index: training and validation set is only from 0:end_index
      val_len: validation length. The last 'val_len' time-points for every time-series is the
          validation set
      shuffle: data is shuffles if True (this is deprecated and set to False)
      """
      n, T = Ymat.shape
      self.vindex = 0
      self.hindex = 0
      self.epoch = 0
      self.vbsize = vbsize
      self.hbsize = hbsize
      self.Ymat = Ymat
      self.val_len = val_len
      self.end_index = end_index
      self.val_index = np.random.randint(0, n - self.vbsize - 5)
      self.shuffle = shuffle
      self.I = np.array(range(n))
      self.covariates = covariates
      self.Ycov = Ycov
  
imports: import numpy
  import torch
label_desc: Use image node_id 1 to create a new TCMFDataLoader object from inherited base classes: object with example: obj = TCMFDataLoader(Ymat, covariates, Ycov, vbsize, hbsize, end_index, val_len, shuffle)