code: def _normalize_by_window_size(dims, strides, padding):
      def rescale(outputs, inputs, spec):
          if spec is None:
              non_spatial_axes = 0, inputs.ndim - 1
          else:
              non_spatial_axes = spec.index("N"), spec.index("C")
  
          spatial_shape = tuple(
              inputs.shape[i]
              for i in range(inputs.ndim)
              if i not in non_spatial_axes
          )
          one = jnp.ones(spatial_shape, dtype=inputs.dtype)
          window_sizes = lax.reduce_window(
              one, 0.0, lax.add, dims, strides, padding
          )
          for i in sorted(non_spatial_axes):
              window_sizes = jnp.expand_dims(window_sizes, i)
  
          return outputs / window_sizes
  
      return rescale
  
imports: import functools
  import operator
  from jax import lax
  from jax import random
  import jax.numpy
  from jax.nn import relu, log_softmax, softmax, softplus, sigmoid, elu, leaky_relu, selu, gelu, standardize
  from jax.nn.initializers import glorot_normal, normal, ones, zeros
label_desc: Use image node_id 7 for calling a global function with example usage: _normalize_by_window_size(dims, strides, padding) and returns: rescale, unknown