code: def __init__(
      self,
      in_channels: int,
      out_channels: int,
      kernel_size: Union[int, Tuple[int, int]],
      stride: Union[int, Tuple[int, int]] = 1,
      padding: Union[int, Tuple[int, int]] = 0,
      dilation: Union[int, Tuple[int, int]] = 1,
      groups: int = 1,
      bias: bool = True,
  ):
      super().__init__(
          in_channels,
          out_channels,
          kernel_size,
          stride,
          0,
          dilation,
          groups,
          bias,
      )
  
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 1 to create a new Conv2dSamePadding object from inherited base classes: nn with example: obj = Conv2dSamePadding(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias)