code: def test_weight_norm_variable_filter(
      self, variable_filters, key_paths
  ):
      class Baz(nn.Module):
          @nn.compact
          def __call__(self, x):
              return nn.Dense(2)(x)
  
      class Bar(nn.Module):
          @nn.compact
          def __call__(self, x):
              x = Baz()(x)
              x = nn.Dense(3)(x)
              x = Baz()(x)
              x = nn.Dense(3)(x)
              return x
  
      for variable_filter in variable_filters:
  
          class Foo(nn.Module):
              @nn.compact
              def __call__(self, x):
                  return nn.WeightNorm(
                      Bar(), variable_filter=variable_filter
                  )(x)
  
          v = Foo().init(jax.random.key(0), jnp.ones((1, 4)))
          self.assertEqual(
              key_paths, v["params"]["WeightNorm_0"].keys()
          )
  
imports: import copy
  import functools
  from typing import Any
  import jax
  import jax.numpy
  import numpy
  import optax
  from absl.testing import absltest, parameterized
  from jax import random
  from flax import ids
  from flax import linen
  from flax.linen import fp8_ops
  from flax.training import train_state
label_desc: Use image node_id 18 for calling the NormalizationTest obj's underlying member method code with example usage: obj.test_weight_norm_variable_filter(variable_filters, key_paths) and returns: nn, x, nn