code: def unfold(
      tensor: torch.Tensor, size: int, step: int, dilation: int = 1
  ) -> torch.Tensor:
      """
      similar to tensor.unfold, but with the dilation
      and specialized for 1d tensors
  
      Returns all consecutive windows of `size` elements, with
      `step` between windows. The distance between each element
      in a window is given by `dilation`.
      """
      if tensor.dim() != 1:
          raise ValueError(
              f"tensor should have 1 dimension instead of {tensor.dim()}"
          )
      o_stride = tensor.stride(0)
      numel = tensor.numel()
      new_stride = (step * o_stride, dilation * o_stride)
      new_size = (
          (numel - (dilation * (size - 1) + 1)) // step + 1,
          size,
      )
      if new_size[0] < 1:
          new_size = (0, size)
      return torch.as_strided(tensor, new_size, new_stride)
  
imports: import bisect
  import math
  import warnings
  from fractions import Fraction
  from typing import Any, Callable, cast, Dict, List, Optional, Tuple, TypeVar, Union
  import torch
  from torchvision.io import _probe_video_from_file, _read_video_from_file, read_video, read_video_timestamps
  from .utils import tqdm
label_desc: Use image node_id 2 for calling a global function with example usage: unfold(tensor, size, step, dilation) and returns: torch