def forward(self, x: Tensor, offset: Tensor) -> Tensor:
        """Deformable Convolutional forward function.

        Args:
            x (Tensor): Input feature, shape (B, C_in, H_in, W_in)
            offset (Tensor): Offset for deformable convolution, shape
                (B, deform_groups*kernel_size[0]*kernel_size[1]*2,
                H_out, W_out), H_out, W_out are equal to the output's.

                An offset is like `[y0, x0, y1, x1, y2, x2, ..., y8, x8]`.
                The spatial arrangement is like:

                .. code:: text

                    (x0, y0) (x1, y1) (x2, y2)
                    (x3, y3) (x4, y4) (x5, y5)
                    (x6, y6) (x7, y7) (x8, y8)

        Returns:
            Tensor: Output of the layer.
        """
        # To fix an assert error in deform_conv_cuda.cpp:128
        # input image is smaller than kernel
        input_pad = (x.size(2) < self.kernel_size[0]) or (x.size(3) <
                                                          self.kernel_size[1])
        if input_pad:
            pad_h = max(self.kernel_size[0] - x.size(2), 0)
            pad_w = max(self.kernel_size[1] - x.size(3), 0)
            x = F.pad(x, (0, pad_w, 0, pad_h), 'constant', 0).contiguous()
            offset = F.pad(offset, (0, pad_w, 0, pad_h), 'constant', 0)
            offset = offset.contiguous()
        out = deform_conv2d(x, offset, self.weight, self.stride, self.padding,
                            self.dilation, self.groups, self.deform_groups,
                            False, self.im2col_step)
        if input_pad:
            out = out[:, :, :out.size(2) - pad_h, :out.size(3) -
                      pad_w].contiguous()
        return out
Deformable Convolutional forward function.

        Args:
            x (Tensor): Input feature, shape (B, C_in, H_in, W_in)
            offset (Tensor): Offset for deformable convolution, shape
                (B, deform_groups*kernel_size[0]*kernel_size[1]*2,
                H_out, W_out), H_out, W_out are equal to the output's.

                An offset is like `[y0, x0, y1, x1, y2, x2, ..., y8, x8]`.
                The spatial arrangement is like:

                .. code:: text

                    (x0, y0) (x1, y1) (x2, y2)
                    (x3, y3) (x4, y4) (x5, y5)
                    (x6, y6) (x7, y7) (x8, y8)

        Returns:
            Tensor: Output of the layer.
        
ControlNet-v1-1-nightly/annotator/uniformer/mmcv/ops/deform_conv.py