class_docstr: A simple wrapper that return and Avg Pool feature pyramid based on the provided scales.
  Implicitly returns the input as well.
class_docstr_tok: ["A","simple","wrapper","that","return","and","Avg","Pool","feature","pyramid","based","on","the","provided","scales",".","Implicitly","returns","the","input","as","well","."]
code: def forward(self, x: torch.Tensor) -> List[Tensor]:
      results = [x]
      for factor in self.factors:
          results.append(
              F.avg_pool2d(x, kernel_size=factor, stride=factor)
          )
      return results
  
imports: import math
  from functools import partial
  from typing import Callable, Dict, Iterable, List, Optional, Tuple
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import torchvision.models.optical_flow.raft
  from torch import Tensor
  from torchvision.models._api import register_model, Weights, WeightsEnum
  from torchvision.models._utils import handle_legacy_interface
  from torchvision.models.optical_flow._utils import grid_sample, make_coords_grid, upsample_flow
  from torchvision.ops import Conv2dNormActivation
  from torchvision.prototype.transforms._presets import StereoMatching
label_desc: Use image node_id 2 for calling the PyramidDownsample obj's underlying member method code with example usage: obj.forward(x) and returns: results