code: def same_padding_transpose_after_stride(
      strided_size: Tuple[int, int],
      kernel: Tuple[int, int],
      stride: Union[int, Tuple[int, int]],
  ) -> (Union[int, Tuple[int, int]], Tuple[int, int]):
      """Computes padding and output size such that TF Conv2DTranspose `same` is matched.
  
      Note that when padding="same", TensorFlow's Conv2DTranspose makes sure that
      0-padding is added to the already strided image in such a way that the output image
      has the same size as the input image times the stride (and no matter the
      kernel size).
  
      For example: Input image is (4, 4, 24) (not yet strided), padding is "same",
      stride=2, kernel=5.
  
      First, the input image is strided (with stride=2):
  
      Input image (4x4):
      A B C D
      E F G H
      I J K L
      M N O P
  
      Stride with stride=2 -> (7x7)
      A 0 B 0 C 0 D
      0 0 0 0 0 0 0
      E 0 F 0 G 0 H
      0 0 0 0 0 0 0
      I 0 J 0 K 0 L
      0 0 0 0 0 0 0
      M 0 N 0 O 0 P
  
      Then this strided image (strided_size=7x7) is padded (exact padding values will be
      output by this function):
  
      padding -> (left=3, right=2, top=3, bottom=2)
  
      0 0 0 0 0 0 0 0 0 0 0 0
      0 0 0 0 0 0 0 0 0 0 0 0
      0 0 0 0 0 0 0 0 0 0 0 0
      0 0 0 A 0 B 0 C 0 D 0 0
      0 0 0 0 0 0 0 0 0 0 0 0
      0 0 0 E 0 F 0 G 0 H 0 0
      0 0 0 0 0 0 0 0 0 0 0 0
      0 0 0 I 0 J 0 K 0 L 0 0
      0 0 0 0 0 0 0 0 0 0 0 0
      0 0 0 M 0 N 0 O 0 P 0 0
      0 0 0 0 0 0 0 0 0 0 0 0
      0 0 0 0 0 0 0 0 0 0 0 0
  
      Then deconvolution with kernel=5 yields an output image of 8x8 (x num output
      filters).
  
      Args:
          strided_size: The size (width x height) of the already strided image.
          kernel: Either width x height (tuple of ints) or - if a square kernel is used -
              a single int for both width and height.
          stride: Either stride width x stride height (tuple of ints) or - if square
              striding is used - a single int for both width- and height striding.
  
      Returns:
          Tuple consisting of 1) `padding`: A 4-tuple to pad the input after(!) striding.
          The values are for left, right, top, and bottom padding, individually.
          This 4-tuple can be used in a torch.nn.ZeroPad2d layer, and 2) the output shape
          after striding, padding, and the conv transpose layer.
      """
  
      # Solve single int (squared) inputs for kernel and/or stride.
      k_w, k_h = (kernel, kernel) if isinstance(kernel, int) else kernel
      s_w, s_h = (stride, stride) if isinstance(stride, int) else stride
  
      # Compute the total size of the 0-padding on both axes. If results are odd numbers,
      # the padding on e.g. left and right (or top and bottom) side will have to differ
      # by 1.
      pad_total_w, pad_total_h = k_w - 1 + s_w - 1, k_h - 1 + s_h - 1
      pad_right = pad_total_w // 2
      pad_left = pad_right + (1 if pad_total_w % 2 == 1 else 0)
      pad_bottom = pad_total_h // 2
      pad_top = pad_bottom + (1 if pad_total_h % 2 == 1 else 0)
  
      # Compute the output size.
      output_shape = (
          strided_size[0] + pad_total_w - k_w + 1,
          strided_size[1] + pad_total_h - k_h + 1,
      )
  
      # Return padding and output shape.
      return (pad_left, pad_right, pad_top, pad_bottom), output_shape
  
imports: import numpy
  from typing import Union, Tuple, Any, List
  from ray.rllib.models.utils import get_activation_fn
  from ray.rllib.utils.annotations import DeveloperAPI
  from ray.rllib.utils.framework import try_import_torch
  from ray.rllib.utils.typing import TensorType
label_desc: Use image node_id 3 for calling a global function with example usage: same_padding_transpose_after_stride(strided_size, kernel, stride) and returns: pad_left,  pad_right,  pad_top,  pad_bottom,  output_shape