docstr: Set the norm of the weight vector.
code: def _init_norm(self):
      """Set the norm of the weight vector."""
      kernel_norm = tf.sqrt(
          tf.reduce_sum(tf.square(self.v), axis=self.kernel_norm_axes)
      )
      self.g.assign(kernel_norm)
  
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 3 for calling the WeightNorm obj's underlying member method code with example usage: obj._init_norm() without return types