code: def get_padding(
      kernel_size: Sequence[int] | int, stride: Sequence[int] | int
  ) -> tuple[int, ...] | int:
      kernel_size_np = np.atleast_1d(kernel_size)
      stride_np = np.atleast_1d(stride)
      padding_np = (kernel_size_np - stride_np + 1) / 2
      if np.min(padding_np) < 0:
          raise AssertionError(
              "padding value should not be negative, please change the kernel size and/or stride."
          )
      padding = tuple(int(p) for p in padding_np)
  
      return padding if len(padding) > 1 else padding[0]
  
imports: from __future__ import annotations
  from collections.abc import Sequence
  import numpy
  import torch
  import torch.nn
  from monai.networks.blocks.convolutions import Convolution
  from monai.networks.layers.factories import Act, Norm
  from monai.networks.layers.utils import get_act_layer, get_norm_layer
label_desc: Use image node_id 2 for calling a global function with example usage: get_padding(kernel_size, stride) and returns: unknown