code: def __init__(
      self,
      output_size,
      output_channels,
      deform_fc_channels=1024,
      spatial_scale=1.0,
      sampling_ratio=0,
      gamma=0.1,
  ):
      super(DeformRoIPoolPack, self).__init__(
          output_size, spatial_scale, sampling_ratio, gamma
      )
  
      self.output_channels = output_channels
      self.deform_fc_channels = deform_fc_channels
  
      self.offset_fc = nn.Sequential(
          nn.Linear(
              self.output_size[0]
              * self.output_size[1]
              * self.output_channels,
              self.deform_fc_channels,
          ),
          nn.ReLU(inplace=True),
          nn.Linear(self.deform_fc_channels, self.deform_fc_channels),
          nn.ReLU(inplace=True),
          nn.Linear(
              self.deform_fc_channels,
              self.output_size[0] * self.output_size[1] * 2,
          ),
      )
      self.offset_fc[-1].weight.data.zero_()
      self.offset_fc[-1].bias.data.zero_()
  
imports: from torch import nn
  from torch.autograd import Function
  from torch.autograd.function import once_differentiable
  from torch.nn.modules.utils import _pair
  from ..utils import ext_loader
label_desc: Use image node_id 1 to create a new DeformRoIPoolPack object from inherited base classes: DeformRoIPool with example: obj = DeformRoIPoolPack(output_size, output_channels, deform_fc_channels, spatial_scale, sampling_ratio, gamma)