class_docstr: Pyramidal optical flow estimation.
  
  This estimates the optical flow at `levels` different pyramidal scales. It
  should be trained in conjunction with a pyramidal optical flow supervision
  signal.
  
  Args:
      in_channels: Number of input channels. Typically uses two 3-channel
          images, plus an initial flow with 2 more channels.
      filter_counts: Number of filters for each stage of pyramid. Defaults
          to ``[32, 64, 32, 16, 2]``.
      kernel_size: Kernel size of filters.
      levels: Number of pyramid levels.
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docstr: Pyramid flow structure, average down for each pyramid level.
code: def _decompose_images_to_pyramids(
      self, image1: Tensor, image2: Tensor
  ) -> Tuple[List[Tensor], List[Tensor]]:
      """Pyramid flow structure, average down for each pyramid level."""
      images_1 = [image1]
      images_2 = [image2]
      for _ in range(1, len(self.model_levels)):
          images_1.append(F.avg_pool2d(images_1[-1], 2))
          images_2.append(F.avg_pool2d(images_2[-1], 2))
  
      return images_1, images_2
  
imports: from typing import Any, List, NamedTuple, Optional, Sequence, Tuple
  import torch
  import torch.nn.functional
  from compressai.entropy_models import EntropyBottleneck
  from torch import Tensor, nn
  import neuralcompression.functional
  from neuralcompression.layers import SimplifiedGDN, SimplifiedInverseGDN
label_desc: Use image node_id 2 for calling the DVCPyramidFlowEstimator obj's underlying member method code with example usage: obj._decompose_images_to_pyramids(image1, image2) and returns: images_1,  images_2