class_docstr: Stacks time within the feature dim, so as to behave as a downsampling operation.
class_docstr_tok: ["Stacks","time","within","the","feature","dim",",","so","as","to","behave","as","a","downsampling","operation","."]
code: def forward(
      self, x: List[Tuple[torch.Tensor]]
  ) -> Tuple[torch.Tensor, torch.Tensor]:
      # T, B, U
      x, x_lens = x
      seq = [x]
      for i in range(1, self.factor):
          tmp = torch.zeros_like(x)
          tmp[:-i, :, :] = x[i:, :, :]
          seq.append(tmp)
      x_lens = torch.ceil(x_lens.float() / self.factor).int()
      return torch.cat(seq, dim=2)[:: self.factor, :, :], x_lens
  
imports: import math
  from typing import List, Optional, Tuple, Union
  import numpy
  import torch
  from nemo.utils import logging
label_desc: Use image node_id 2 for calling the StackTime obj's underlying member method code with example usage: obj.forward(x) and returns: torch,  x_lens