docstr: Call `Layer`.
code: def call(self, inputs):
      """Call `Layer`."""
      if not self.initialized:
          if self.data_init:
              self._data_dep_init(inputs)
          else:  # initialize `g` as the norm of the initialized kernel
              self._init_norm()
  
          self.initialized.assign(True)
  
      self._compute_weights()
      output = self.layer(inputs)
      return output
  
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 6 for calling the WeightNorm obj's underlying member method code with example usage: obj.call(inputs) and returns: output