class_docstr: Flow head, part of the update block.
  
  Takes the hidden state of the recurrent unit as input, and outputs the predicted "delta flow".
class_docstr_tok: ["Flow","head",",","part","of","the","update","block",".","Takes","the","hidden","state","of","the","recurrent","unit","as","input",",","and","outputs","the","predicted","``","delta","flow","''","."]
code: def forward(self, x):
      return self.conv2(self.relu(self.conv1(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 FlowHead obj's underlying member method code with example usage: obj.forward(x) and returns: self