code: def max_pool(
      inputs,
      pool_size,
      strides=None,
      padding="valid",
      data_format=None,
  ):
      data_format = standardize_data_format(data_format)
      num_spatial_dims = inputs.ndim - 2
      pool_size = _convert_to_spatial_operand(
          pool_size, num_spatial_dims, data_format
      )
      strides = pool_size if strides is None else strides
      strides = _convert_to_spatial_operand(
          strides, num_spatial_dims, data_format
      )
      return _pool(
          inputs, -jnp.inf, lax.max, pool_size, strides, padding
      )
  
imports: import jax
  import jax.numpy
  import numpy
  from jax import lax
  from jax import nn
  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.jax.core import cast
  from keras.backend.jax.core import convert_to_tensor
label_desc: Use image node_id 19 for calling a global function with example usage: max_pool(inputs, pool_size, strides, padding, data_format) and returns: _pool