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: Calculate optical flow using pyramidal structure and a learned model.
  
  Args:
      image1: The based image - a flow map will be computed to transform
          ``image1`` into ``image2``.
      image2: The target image.
  
  Returns:
      Pyramidal optical flow estimate.
code: def forward(self, image1: Tensor, image2: Tensor) -> Tensor:
      """
      Calculate optical flow using pyramidal structure and a learned model.
  
      Args:
          image1: The based image - a flow map will be computed to transform
              ``image1`` into ``image2``.
          image2: The target image.
  
      Returns:
          Pyramidal optical flow estimate.
      """
      return self.calculate_flow_with_image_pairs(image1, image2)[0]
  
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 4 for calling the DVCPyramidFlowEstimator obj's underlying member method code with example usage: obj.forward(image1, image2) and returns: self