code: def testConv2DInitializedCorrectly(self, transpose):
      conv = tfkl.Conv2DTranspose if transpose else tfkl.Conv2D
      conv_layer = conv(
          filters=self.num_conv_filters,
          kernel_size=(2, 2),
          kernel_initializer="ones",
      )
      norm_layer = weight_norm.WeightNorm(conv_layer, name="norm_layer")
  
      norm_layer.build(self.conv_random_input.shape)
      self.evaluate([v.initializer for v in norm_layer.weights])
      self.evaluate(norm_layer(self.conv_random_input))
  
      (
          true_init_g,
          true_init_bias,
      ) = self._calculate_true_initial_variables_conv(
          self.conv_random_input,
          self.num_conv_filters,
          transpose=transpose,
      )
  
      self.assertAllClose(true_init_g, self.evaluate(norm_layer.g))
      self.assertAllClose(
          true_init_bias,
          self.evaluate(norm_layer.layer.bias),
          rtol=5e-6,
      )
  
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 13 for calling the WeightNormTest obj's underlying member method code with example usage: obj.testConv2DInitializedCorrectly(transpose) without return types