code: def conv_transpose_shape_tuple(
      lhs_shape, rhs_shape, window_strides, padding, dimension_numbers
  ):
      lhs_perm, rhs_perm, out_perm = conv_general_permutations(
          dimension_numbers
      )
      lhs_trans = np.take(lhs_shape, lhs_perm)
      rhs_trans = np.take(rhs_shape, rhs_perm)
      if isinstance(padding, str):
          padding = [
              _conv_transpose_padding(k, s, padding)
              for k, s in zip(rhs_trans[2:], window_strides)
          ]
      padding = list(map(np.sum, padding))
      unpad_out_space = [
          (i - 1) * s - k + 2
          for i, k, s in zip(
              lhs_trans[2:], rhs_trans[2:], window_strides
          )
      ]
      out_space = np.sum([unpad_out_space, padding], axis=0).tolist()
      out_trans = tuple((lhs_trans[0], rhs_trans[0]) + tuple(out_space))
      return tuple(np.take(out_trans, np.argsort(out_perm)))
  
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 18 for calling a global function with example usage: conv_transpose_shape_tuple(lhs_shape, rhs_shape, window_strides, padding, dimension_numbers) and returns: tuple