docstr: Args:
      gamma: value for the exponential weighting of the loss across frames
      max_flow: maximum flow value to exclude
      exclude_large_flows: whether to exclude large flows
code: def __init__(
      self,
      gamma: float = 0.8,
      max_flow: int = 256,
      exclude_large_flows: bool = False,
  ) -> None:
      """
      Args:
          gamma: value for the exponential weighting of the loss across frames
          max_flow: maximum flow value to exclude
          exclude_large_flows: whether to exclude large flows
      """
  
      super().__init__()
      self.max_flow = max_flow
      self.excluding_large = exclude_large_flows
      self.register_buffer("gamma", torch.tensor([gamma]))
      # cache the scale factor for the loss
      self._weights = None
  
imports: from typing import List, Optional
  import torch
  from torch import nn, Tensor
  from torch.nn import functional
  from torchvision.prototype.models.depth.stereo.raft_stereo import grid_sample, make_coords_grid
label_desc: Use image node_id 1 to create a new SequenceLoss object from inherited base classes: nn with example: obj = SequenceLoss(gamma, max_flow, exclude_large_flows)