code: def testShapes(self):
      loc = tf.ones([2], dtype=tf.float32)
      scale = tf.ones([3, 1], dtype=tf.float32)
      tailweight = tf.zeros([4, 1, 1], dtype=tf.float32)
      lwn = lambertw_f.LambertWNormal(
          loc=loc,
          scale=scale,
          tailweight=tailweight,
          validate_args=True,
      )
      self.assertAllEqual(lwn.batch_shape, [4, 3, 2])
  
imports: import math
  from absl.testing import parameterized
  import numpy
  from scipy import stats
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import lambertw_transform
  from tensorflow_probability.python.distributions import lambertw_f
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import student_t
  from tensorflow_probability.python.distributions import transformed_distribution
  from tensorflow_probability.python.distributions import uniform
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.internal import tf_keras
label_desc: Use image node_id 1 for calling the LambertWNormalTest obj's underlying member method code with example usage: obj.testShapes() without return types