code: def get_output_padding(
      kernel_size: Sequence[int] | int,
      stride: Sequence[int] | int,
      padding: Sequence[int] | int,
  ) -> tuple[int, ...] | int:
      kernel_size_np = np.atleast_1d(kernel_size)
      stride_np = np.atleast_1d(stride)
      padding_np = np.atleast_1d(padding)
  
      out_padding_np = 2 * padding_np + stride_np - kernel_size_np
      if np.min(out_padding_np) < 0:
          raise AssertionError(
              "out_padding value should not be negative, please change the kernel size and/or stride."
          )
      out_padding = tuple(int(p) for p in out_padding_np)
  
      return out_padding if len(out_padding) > 1 else out_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 3 for calling a global function with example usage: get_output_padding(kernel_size, stride, padding) and returns: unknown