code: def _conv_transpose_padding(k, s, padding):
      """Calculate before and after padding for a dim of transposed convolution.
  
      Args:
        k: int: kernel dimension.
        s: int: dimension stride value.
        padding: 'same' or 'valid' padding mode for original forward conv.
  
      Returns:
        2-tuple: ints: before and after padding for transposed convolution.
      """
      if padding == "SAME":
          pad_len = k + s - 2
          if s > k - 1:
              pad_a = k - 1
          else:
              pad_a = int(np.ceil(pad_len / 2))
      elif padding == "VALID":
          pad_len = k + s - 2 + max(k - s, 0)
          pad_a = k - 1
      else:
          raise ValueError("Padding mode must be `SAME` or `VALID`.")
      pad_b = pad_len - pad_a
      return pad_a, pad_b
  
imports: from __future__ import annotations
  from collections.abc import Sequence
  from functools import partial
  import operator
  from typing import NamedTuple, Union
  import numpy
  from jax._src import core
  from jax._src import dtypes
  from jax._src import util
  from jax._src.interpreters import ad
  from jax._src.interpreters import batching
  from jax._src.interpreters import mlir
  from jax._src.lax import lax
  from jax._src.lib.mlir.dialects import hlo
  from jax._src.typing import Array, DTypeLike
label_desc: Use image node_id 4 for calling a global function with example usage: _conv_transpose_padding(k, s, padding) and returns: pad_a,  pad_b