docstr: Args:
      flow_preds: list of tensors of shape (batch_size, C, H, W)
  
  Returns:
      sequence consistency loss of shape (batch_size,)
code: def forward(self, flow_preds: List[Tensor]) -> Tensor:
      """
      Args:
          flow_preds: list of tensors of shape (batch_size, C, H, W)
  
      Returns:
          sequence consistency loss of shape (batch_size,)
      """
      loss, weights = _flow_sequence_consistency_loss_fn(
          flow_preds,
          gamma=self.gamma,
          resize_factor=self.resize_factor,
          rescale_factor=self.rescale_factor,
          rescale_mode=self.rescale_mode,
          weights=self._weights,
      )
      self._weights = weights
      return loss
  
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 2 for calling the FlowSequenceConsistencyLoss obj's underlying member method code with example usage: obj.forward(flow_preds) and returns: loss