code: def _pool(
      inputs,
      initial_value,
      reduce_fn,
      pool_size,
      strides=None,
      padding="valid",
  ):
      """Helper function to define pooling functions.
  
      Args:
          inputs: input data of shape `N+2`.
          initial_value: the initial value for the reduction.
          reduce_fn: a reduce function of the form `(T, T) -> T`.
          pool_size: a sequence of `N` integers, representing the window size to
              reduce over.
          strides: a sequence of `N` integers, representing the inter-window
              strides (default: `(1, ..., 1)`).
          padding: either the string `same` or `valid`.
  
      Returns:
          The output of the reduction for each window slice.
      """
      if padding not in ("same", "valid"):
          raise ValueError(
              f"Invalid padding '{padding}', must be 'same' or 'valid'."
          )
      padding = padding.upper()
      return np.array(
          lax.reduce_window(
              inputs,
              initial_value,
              reduce_fn,
              pool_size,
              strides,
              padding,
          )
      )
  
imports: import jax
  import numpy
  from jax import lax
  from jax import numpy
  from keras.backend import standardize_data_format
  from keras.backend import standardize_dtype
  from keras.backend.common.backend_utils import compute_conv_transpose_padding_args_for_jax
  from keras.backend.config import epsilon
  from keras.backend.numpy.core import cast
  from keras.backend.numpy.core import convert_to_tensor
  from keras.backend.numpy.core import is_tensor
  from keras.utils.module_utils import scipy
label_desc: Use image node_id 18 for calling a global function with example usage: _pool(inputs, initial_value, reduce_fn, pool_size, strides, padding) and returns: np