class_docstr: Data Loader Class for DeepGLO
docstr: Supplies validation set in the same format as above
docstr_tok: ["Supplies","validation","set","in","the","same","format","as","above"]
code: def supply_test(self):
      """
      Supplies validation set in the same format as above
      """
      n, T = self.Ymat.shape
      index = self.val_index
      in_data = self.Ymat[
          int(index) : int(index + self.vbsize),
          int(self.end_index) : int(self.end_index + self.val_len),
      ]
      out_data = self.Ymat[
          int(index) : int(index + self.vbsize),
          int(self.end_index + 1) : int(
              self.end_index + self.val_len + 1
          ),
      ]
      nd, Td = in_data.shape
      if self.covariates is not None:
          covs = self.covariates[
              :,
              int(self.end_index) : int(self.end_index + self.val_len),
          ]
          rcovs = np.repeat(
              covs.reshape(1, covs.shape[0], covs.shape[1]),
              repeats=nd,
              axis=0,
          )
      if self.Ycov is not None:
          ycovs = self.Ycov[
              int(index) : int(index + self.vbsize),
              :,
              int(self.end_index) : int(self.end_index + self.val_len),
          ]
      inp = torch.from_numpy(in_data).view(1, nd, Td)
      inp = inp.transpose(0, 1).float()
      if self.covariates is not None:
          rcovs = torch.from_numpy(rcovs).float()
      if self.Ycov is not None:
          ycovs = torch.from_numpy(ycovs).float()
      out = torch.from_numpy(out_data).view(1, nd, Td)
      if self.covariates is not None:
          inp = torch.cat((inp, rcovs), 1)
      if self.Ycov is not None:
          inp = torch.cat((inp, ycovs), 1)
      out = out.transpose(0, 1).float()
      return inp, out, self.vindex, self.hindex
  
imports: import numpy
  import torch
label_desc: Use image node_id 3 for calling the TCMFDataLoader obj's underlying member method code with example usage: obj.supply_test() and returns: inp,  out,  self,  self