code: def pool(inputs, init, reduce_fn, window_shape, strides, padding):
      """Helper function to define pooling functions.
  
      Pooling functions are implemented using the ReduceWindow XLA op.
      NOTE: Be aware that pooling is not generally differentiable.
      That means providing a reduce_fn that is differentiable does not imply that
      pool is differentiable.
  
      Args:
        inputs: input data with dimensions (batch, window dims..., features).
        init: the initial value for the reduction
        reduce_fn: a reduce function of the form ``(T, T) -> T``.
        window_shape: a shape tuple defining the window to reduce over.
        strides: a sequence of ``n`` integers, representing the inter-window
          strides (default: ``(1, ..., 1)``).
        padding: either the string ``'SAME'``, the string ``'VALID'``, or a sequence
          of ``n`` ``(low, high)`` integer pairs that give the padding to apply before
          and after each spatial dimension.
      Returns:
        The output of the reduction for each window slice.
      """
      num_batch_dims = inputs.ndim - (len(window_shape) + 1)
      strides = strides or (1,) * len(window_shape)
      assert len(window_shape) == len(
          strides
      ), f"len({window_shape}) must equal len({strides})"
      strides = (1,) * num_batch_dims + strides + (1,)
      dims = (1,) * num_batch_dims + window_shape + (1,)
  
      is_single_input = False
      if num_batch_dims == 0:
          # add singleton batch dimension because lax.reduce_window always
          # needs a batch dimension.
          inputs = inputs[None]
          strides = (1,) + strides
          dims = (1,) + dims
          is_single_input = True
  
      assert inputs.ndim == len(
          dims
      ), f"len({inputs.shape}) != len({dims})"
      if not isinstance(padding, str):
          padding = tuple(map(tuple, padding))
          assert len(padding) == len(window_shape), (
              f"padding {padding} must specify pads for same number of dims as "
              f"window_shape {window_shape}"
          )
          assert all(
              [len(x) == 2 for x in padding]
          ), f"each entry in padding {padding} must be length 2"
          padding = ((0, 0),) + padding + ((0, 0),)
      y = lax.reduce_window(
          inputs, init, reduce_fn, dims, strides, padding
      )
      if is_single_input:
          y = jnp.squeeze(y, axis=0)
      return y
  
imports: import jax.numpy
  import numpy
  from jax import lax
label_desc: Use image node_id 1 for calling a global function with example usage: pool(inputs, init, reduce_fn, window_shape, strides, padding) and returns: y