docstr: Generate weights with normalization.
code: def _compute_weights(self):
      """Generate weights with normalization."""
      # Determine the axis along which to expand `g` so that `g` broadcasts to
      # the shape of `v`.
      new_axis = -self.filter_axis - 3
  
      self.layer.kernel = tf.nn.l2_normalize(
          self.v, axis=self.kernel_norm_axes
      ) * tf.expand_dims(self.g, new_axis)
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import functools
  import numpy
  import warnings
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import bijector
  from tensorflow_probability.python.distributions import categorical
  from tensorflow_probability.python.distributions import distribution
  from tensorflow_probability.python.distributions import independent
  from tensorflow_probability.python.distributions import logistic
  from tensorflow_probability.python.distributions import mixture_same_family
  from tensorflow_probability.python.distributions import quantized_distribution
  from tensorflow_probability.python.distributions import transformed_distribution
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import reparameterization
  from tensorflow_probability.python.internal import tensor_util
  from tensorflow_probability.python.internal import tensorshape_util
label_desc: Use image node_id 2 for calling the WeightNorm obj's underlying member method code with example usage: obj._compute_weights() without return types