class_docstr: Mask predictor to be used when upsampling the predicted flow.
  
  It takes the hidden state of the recurrent unit as input and outputs the mask.
  This is not used in the raft-small model.
class_docstr_tok: ["Mask","predictor","to","be","used","when","upsampling","the","predicted","flow",".","It","takes","the","hidden","state","of","the","recurrent","unit","as","input","and","outputs","the","mask",".","This","is","not","used","in","the","raft-small","model","."]
code: def forward(self, x):
      x = self.convrelu(x)
      x = self.conv(x)
      return self.multiplier * x
  
imports: from typing import List, Optional
  import torch
  import torch.nn
  import torch.nn.functional
  from torch import Tensor
  from torch.nn.modules.batchnorm import BatchNorm2d
  from torch.nn.modules.instancenorm import InstanceNorm2d
  from torchvision.ops import Conv2dNormActivation
  from ...transforms._presets import OpticalFlow
  from ...utils import _log_api_usage_once
  from .._api import register_model, Weights, WeightsEnum
  from .._utils import handle_legacy_interface
  from ._utils import grid_sample, make_coords_grid, upsample_flow
label_desc: Use image node_id 2 for calling the MaskPredictor obj's underlying member method code with example usage: obj.forward(x) and returns: unknown