code: def conv2d_same(
      input_, kernel, groups, bias=None, stride=1, padding=0, dilation=1
  ):
      n, c, h, w = input_.shape
      kout, ki_c_g, kh, kw = kernel.shape
      pw = calc_pad_same(w, w, 1, kw)
      ph = calc_pad_same(h, h, 1, kh)
      pw_l = pw // 2
      pw_r = pw - pw_l
      ph_t = ph // 2
      ph_b = ph - ph_t
  
      input_ = F.pad(input_, (pw_l, pw_r, ph_t, ph_b))
      result = F.conv2d(
          input_,
          kernel,
          bias=bias,
          stride=stride,
          padding=padding,
          dilation=dilation,
          groups=groups,
      )
      if result.shape != input_.shape:
          raise AssertionError
      return result
  
file_path: pywick/pywick/models/segmentation/testnets/gscnn/my_functionals/custom_functional.py
imports: import torch
  import torch.nn.functional
  from torchvision.transforms.functional import pad
  import numpy
label_desc: Use image node_id 2 for calling a global function with example usage: conv2d_same(input_, kernel, groups, bias, stride, padding, dilation) and returns: result