code: def forward(self, x):
      x_shape = x.size()  # (b, c, h, w)
      offset = self.offset_filter(x)  # (b, 2*c, h, w)
      offset_w, offset_h = torch.split(
          offset, self.regular_filter.in_channels, 1
      )  # (b, c, h, w)
      offset_w = offset_w.contiguous().view(
          -1, int(x_shape[2]), int(x_shape[3])
      )  # (b*c, h, w)
      offset_h = offset_h.contiguous().view(
          -1, int(x_shape[2]), int(x_shape[3])
      )  # (b*c, h, w)
      if not self.input_shape or self.input_shape != x_shape:
          self.input_shape = x_shape
          grid_w, grid_h = np.meshgrid(
              np.linspace(-1, 1, x_shape[3]),
              np.linspace(-1, 1, x_shape[2]),
          )  # (h, w)
          grid_w = torch.Tensor(grid_w)
          grid_h = torch.Tensor(grid_h)
          if self.cuda:
              grid_w = grid_w.cuda()
              grid_h = grid_h.cuda()
          self.grid_w = nn.Parameter(grid_w)
          self.grid_h = nn.Parameter(grid_h)
      offset_w = offset_w + self.grid_w  # (b*c, h, w)
      offset_h = offset_h + self.grid_h  # (b*c, h, w)
      x = (
          x.contiguous()
          .view(-1, int(x_shape[2]), int(x_shape[3]))
          .unsqueeze(1)
      )  # (b*c, 1, h, w)
      x = F.grid_sample(
          x, torch.stack((offset_h, offset_w), 3)
      )  # (b*c, h, w)
      x = x.contiguous().view(
          -1, int(x_shape[1]), int(x_shape[2]), int(x_shape[3])
      )  # (b, c, h, w)
      x = self.regular_filter(x)
      return x
  
imports: import os
  from math import ceil
  import numpy
  import torch
  import torch.nn.functional
  from torch import nn
label_desc: Use image node_id 2 for calling the Conv2dDeformable obj's underlying member method code with example usage: obj.forward(x) and returns: x