code: def conv2d_same(
      x,
      weight: torch.Tensor,
      bias: Optional[torch.Tensor] = None,
      stride: Tuple[int, int] = (1, 1),
      padding: Tuple[int, int] = (0, 0),
      dilation: Tuple[int, int] = (1, 1),
      groups: int = 1,
  ):
      ih, iw = x.size()[-2:]
      kh, kw = weight.size()[-2:]
      pad_h = _calc_same_pad(ih, kh, stride[0], dilation[0])
      pad_w = _calc_same_pad(iw, kw, stride[1], dilation[1])
      x = F.pad(
          x,
          [
              pad_w // 2,
              pad_w - pad_w // 2,
              pad_h // 2,
              pad_h - pad_h // 2,
          ],
      )
      return F.conv2d(x, weight, bias, stride, (0, 0), dilation, groups)
  
imports: import collections.abc
  import math
  from functools import partial
  from itertools import repeat
  from typing import Tuple, Optional
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from .config import *
label_desc: Use image node_id 7 for calling a global function with example usage: conv2d_same(x, weight, bias, stride, padding, dilation, groups) and returns: F