code: def max_pool(
      inputs: jnp.ndarray,
      window_shape: Tuple[int, ...],
      strides: Optional[Tuple[int, ...]] = None,
      padding: Union[str, Sequence[Tuple[int, int]]] = "VALID",
      spatial_shape: Optional[jnp.ndarray] = None,
  ) -> Tuple[jnp.ndarray, Optional[jnp.ndarray]]:
      """Pools the input by taking the maximum of a window slice.
  
      Args:
        inputs: Input data with dimensions (batch, window_dims..., features).
        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 (default: `'VALID'`).
        spatial_shape: Per-input spatial shape of *unpadded* input data with
          dimensions (batch, window_dims).
  
      Returns:
        The maximum for each window slice.
      """
      if spatial_shape is not None:
          # Unlike for avg pool, for max pool a spatial mask must be applied first.
          inputs = apply_spatial_mask(
              inputs, spatial_shape, value=-jnp.inf
          )
  
      inputs = nn.max_pool(
          inputs, window_shape, strides=strides, padding=padding
      )
      if spatial_shape is not None:
          if (
              isinstance(padding, str)
              and padding.upper() == "SAME"
              and window_shape != (1,) * len(window_shape)
          ):
              raise NotImplementedError(
                  "Padding 'SAME' is not supported by masked max pool."
              )
          spatial_shape = _conv_output_shape(
              spatial_shape=spatial_shape,
              kernel_size=window_shape,
              input_dilation=None,
              kernel_dilation=None,
              strides=strides,
              padding=padding,
          )
          inputs = apply_spatial_mask(inputs, spatial_shape)
  
      return inputs, spatial_shape
  
imports: import functools
  from typing import Optional, Tuple, Union, Callable, Any, Sequence, List, Iterable
  import flax.linen
  import jax
  from jax import lax
  import jax.numpy
  import numpy
label_desc: Use image node_id 3 for calling a global function with example usage: max_pool(inputs, window_shape, strides, padding, spatial_shape) and returns: inputs,  spatial_shape