code: def reduce_window(
      operand,
      init_value,
      computation: Callable,
      window_dimensions: core.Shape,
      window_strides: Sequence[int],
      padding: str | Sequence[tuple[int, int]],
      base_dilation: Sequence[int] | None = None,
      window_dilation: Sequence[int] | None = None,
  ) -> Array:
      """Wraps XLA's `ReduceWindowWithGeneralPadding
      <https://www.tensorflow.org/xla/operation_semantics#reducewindow>`_
      operator.
      """
      flat_operands, operand_tree = tree_util.tree_flatten(operand)
      flat_init_values, init_value_tree = tree_util.tree_flatten(
          init_value
      )
      if operand_tree != init_value_tree:
          raise ValueError(
              "Operands must have the same tree structure as "
              f"init_values: {operand_tree} vs. {init_value_tree}"
          )
      if len(flat_operands) == 0:
          raise ValueError(
              "reduce_window must have at least one operand."
          )
      if len(flat_operands) != len(flat_init_values):
          raise ValueError(
              "Must have same total number of operands as init_values: "
              f" {len(flat_operands)} vs. {len(flat_init_values)}"
          )
      if isinstance(padding, str):
          dilated_window_dims = (
              window_dimensions
              if window_dilation is None
              else lax._dilate_shape(window_dimensions, window_dilation)
          )
          padding = tuple(
              lax.padtype_to_pads(
                  flat_operands[0].shape,
                  dilated_window_dims,
                  window_strides,
                  padding,
              )
          )
      else:
          padding = tuple(padding)
      if base_dilation is None:
          base_dilation = (1,) * len(window_dimensions)
      if window_dilation is None:
          window_dilation = (1,) * len(window_dimensions)
      monoid_reducer = _get_monoid_window_reducer(
          computation, flat_init_values
      )
      if monoid_reducer:
          return monoid_reducer(
              operand,
              window_dimensions,
              window_strides,
              padding,
              base_dilation,
              window_dilation,
          )
      else:
          flat_init_avals = map(lax._abstractify, flat_init_values)
          jaxpr, out_tree = lax._variadic_reduction_jaxpr(
              computation, tuple(flat_init_avals), init_value_tree
          )
          if operand_tree != out_tree:
              raise ValueError(
                  "reduce_window output must have the same tree structure as the operands"
                  f" {operand_tree} vs. {out_tree}"
              )
          out_flat = reduce_window_p.bind(
              *flat_operands,
              *flat_init_values,
              jaxpr=jaxpr.jaxpr,
              consts=tuple(jaxpr.consts),
              window_dimensions=tuple(window_dimensions),
              window_strides=tuple(window_strides),
              padding=padding,
              base_dilation=tuple(base_dilation),
              window_dilation=tuple(window_dilation),
          )
          return tree_util.tree_unflatten(out_tree, out_flat)
  
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 1 for calling a global function with example usage: reduce_window(operand, init_value, computation, window_dimensions, window_strides, padding, base_dilation, window_dilation) and returns: monoid_reducer, tree_util