code: def get_same_padding(x: int, k: int, s: int, d: int) -> int:
      """
      Calculate asymmetric TensorFlow-like 'SAME' padding for a convolution
      Args:
          x(`Int`):
              Input tensor shape.
          k(`Int`):
              Convolution kernel size.
          s(`Int`):
              Convolution stride parameter.
          d(`Int`):
              Convolution dilation parameter.
      Returns:
          (`Int`):
              Padding value for 'SAME' padding.
      """
      return max((math.ceil(x / s) - 1) * s + (k - 1) * d + 1 - x, 0)
  
file_path: towhee/towhee/models/layers/padding_functions.py
imports: import math
  from typing import List, Tuple
  import torch
  from torch import Tensor
  import torch.nn.functional
label_desc: Use image node_id 2 for calling a global function with example usage: get_same_padding(x, k, s, d) and returns: max