code: def unfold(input, kernel_size: int, stride: int):
      """1D only unfolding similar to the one from PyTorch.
      However PyTorch unfold is extremely slow.
  
      Given an input tensor of size `[*, T]` this will return
      a tensor `[*, F, K]` with `K` the kernel size, and `F` the number
      of frames. The i-th frame is a view onto `i * stride: i * stride + kernel_size`.
      This will automatically pad the input to cover at least once all entries in `input`.
  
      Args:
          input (Tensor): tensor for which to return the frames.
          kernel_size (int): size of each frame.
          stride (int): stride between each frame.
  
      Shape:
  
          - Inputs: `input` is `[*, T]`
          - Output: `[*, F, kernel_size]` with `F = 1 + ceil((T - kernel_size) / stride)`
  
  
      ..Warning:: unlike PyTorch unfold, this will pad the input
          so that any position in `input` is covered by at least one frame.
      """
      shape = list(input.shape)
      length = shape.pop(-1)
      n_frames = (
          math.ceil((max(length, kernel_size) - kernel_size) / stride)
          + 1
      )
      tgt_length = (n_frames - 1) * stride + kernel_size
      padded = F.pad(input, (0, tgt_length - length)).contiguous()
      strides: tp.List[int] = []
      for dim in range(padded.dim()):
          strides.append(padded.stride(dim))
      assert strides.pop(-1) == 1, "data should be contiguous"
      strides = strides + [stride, 1]
      return padded.as_strided(shape + [n_frames, kernel_size], strides)
  
file_path: julius/julius/core.py
imports: import math
  import typing
  import torch
  from torch.nn import functional
label_desc: Use image node_id 8 for calling a global function with example usage: unfold(input, kernel_size, stride) and returns: padded