code: def testCorrectInitialValues(self, model_type):
      (
          true_init_g,
          true_init_bias,
      ) = self._calculate_true_initial_variables_dense(
          self.random_input, self.data_dim, self.num_hidden
      )
  
      # Build the model and call it on a batch of data to trigger initialization
      model = self._define_model(
          model_type, self.data_dim, self.num_hidden
      )
      model.build(self.random_input.shape)
      self.evaluate([v.initializer for v in model.weights])
      norm_layer = self._get_maybe_norm_layer(model)
      self.evaluate(model(self.random_input))
  
      self.assertAllClose(true_init_g, self.evaluate(norm_layer.g))
      self.assertAllClose(
          true_init_bias, self.evaluate(norm_layer.layer.bias)
      )
      self.assertTrue(self.evaluate(norm_layer.initialized))
  
      # Make sure variables are not re-initialized with a second batch of data
      new_input = np.random.rand(self.batch_size, self.data_dim) + 5.0
      self.evaluate(model(new_input))
      self.assertAllClose(true_init_g, self.evaluate(norm_layer.g))
      self.assertAllClose(
          true_init_bias, self.evaluate(norm_layer.layer.bias)
      )
  
imports: import tempfile
  from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.internal import tf_keras
  from tensorflow_probability.python.layers import weight_norm
label_desc: Use image node_id 9 for calling the WeightNormTest obj's underlying member method code with example usage: obj.testCorrectInitialValues(model_type) without return types