code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      img_h, img_w = x.size()[-2:]
      kernel_h, kernel_w = self.weight.size()[-2:]
      extra_w = (
          (math.ceil(img_w / self.stride[1]) - 1) * self.stride[1]
          - img_w
          + kernel_w
      )
      extra_h = (
          (math.ceil(img_h / self.stride[0]) - 1) * self.stride[0]
          - img_h
          + kernel_h
      )
  
      left = extra_w // 2
      right = extra_w - left
      top = extra_h // 2
      bottom = extra_h - top
      x = F.pad(x, [left, right, top, bottom])
      return F.conv2d(
          x,
          self.weight,
          self.bias,
          self.stride,
          self.padding,
          self.dilation,
          self.groups,
      )
  
imports: import math
  from typing import Tuple, Union
  import torch
  import torch.nn
  from mmcv.cnn.bricks import Swish, build_norm_layer
  from torch.nn import functional
  from torch.nn.init import _calculate_fan_in_and_fan_out, trunc_normal_
  from mmdet.registry import MODELS
  from mmdet.utils import OptConfigType
label_desc: Use image node_id 2 for calling the Conv2dSamePadding obj's underlying member method code with example usage: obj.forward(x) and returns: F