code: def _reshape_reduce_window(
      operand,
      operand_shape,
      window_dimensions,
      window_strides,
      window_dilation,
      *,
      has_only_spatial_dims,
  ):
      # Reshape inputs so they are accepted by tf.nn.pool, which expects batch and
      # channel dimensions for operand but not for any of the other inputs.
      if has_only_spatial_dims:  # len(operand_shape) <= 2
          # Call eval_shape on a shape that may contain polynomials, otherwise TF does
          # not know what to do with polynomials in the shape.
          operand_shape = jax2tf._eval_shape(operand_shape)
          # Add batch and channel dimensions to operand.
          operand = tf.reshape(operand, (1,) + operand_shape + (1,))
      else:
          # This branch assumes operand.shape = (batch, spatial_dims, ..., channel),
          # and dimensions, strides, dilation are all (1, spatial_values, ..., 1).
          # Input validation for this is done in _validate_reduce_window_inputs.
          window_dimensions = window_dimensions[1:-1]
          window_strides = window_strides[1:-1]
          window_dilation = window_dilation[1:-1]
  
      return operand, window_dimensions, window_strides, window_dilation
  
imports: from __future__ import annotations
  import builtins
  from collections.abc import Sequence
  import dataclasses
  from functools import partial, wraps
  import math
  import string
  from typing import Any, Callable, Optional
  from jax._src import core
  from jax import lax
  from jax._src.lax import slicing
  from jax._src import dtypes
  from jax._src import util
  from jax.experimental.jax2tf import jax2tf
  import numpy
  import tensorflow
label_desc: Use image node_id 21 for calling a global function with example usage: _reshape_reduce_window(operand, operand_shape, window_dimensions, window_strides, window_dilation) and returns: operand,  window_dimensions,  window_strides,  window_dilation