code: def reduce_window_shape_tuple(
      operand_shape,
      window_dimensions,
      window_strides,
      padding,
      base_dilation=None,
      window_dilation=None,
  ):
      if base_dilation is not None:
          operand_shape = lax._dilate_shape(
              operand_shape, base_dilation
          )
      if window_dilation is not None:
          window_dimensions = lax._dilate_shape(
              window_dimensions, window_dilation
          )
      operand_padded = tuple(
          d + pl + ph for d, (pl, ph) in zip(operand_shape, padding)
      )
      return tuple(
          map(
              core.stride_dim,
              operand_padded,
              window_dimensions,
              window_strides,
          )
      )
  
imports: from __future__ import annotations
  from collections.abc import Sequence
  from functools import partial
  from typing import Callable
  import warnings
  import numpy
  from jax import tree_util
  from jax._src import ad_util
  from jax._src import core
  from jax._src import dispatch
  from jax._src import dtypes
  from jax._src import util
  from jax._src.core import ShapedArray, ConcreteArray
  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.lax import convolution
  from jax._src.lax import slicing
  from jax._src.lib.mlir import ir
  from jax._src.lib.mlir.dialects import hlo
  from jax._src.numpy.ufuncs import logaddexp
  from jax._src.typing import Array
label_desc: Use image node_id 19 for calling a global function with example usage: reduce_window_shape_tuple(operand_shape, window_dimensions, window_strides, padding, base_dilation, window_dilation) and returns: tuple