code: def testChangingVariableShapes(self):
      if not tf.executing_eagerly():
          return
  
      loc = tf.Variable(
          np.zeros((4, 5, 2, 3)), shape=tf.TensorShape(None)
      )
      scale = tf.Variable(np.ones([]), shape=tf.TensorShape(None))
      dist = independent.Independent(
          logistic.Logistic(loc=loc, scale=scale),
          reinterpreted_batch_ndims=None,
          validate_args=True,
      )
  
      self.assertAllEqual((4,), dist.batch_shape_tensor())
  
      loc.assign(np.zeros((3, 7, 1, 1, 1)))
      self.assertAllEqual((3,), dist.batch_shape_tensor())
      self.assertAllEqual(
          (2, 3), tf.shape(dist.log_prob(np.zeros((2, 3, 7, 1, 1, 1))))
      )
  
imports: import functools
  import os
  from absl.testing import parameterized
  import numpy
  from scipy import stats
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import bernoulli
  from tensorflow_probability.python.distributions import exponential
  from tensorflow_probability.python.distributions import independent
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.distributions import log_prob_ratio
  from tensorflow_probability.python.distributions import logistic
  from tensorflow_probability.python.distributions import lognormal
  from tensorflow_probability.python.distributions import mvn_diag
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import poisson
  from tensorflow_probability.python.internal import hypothesis_testlib
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import test_util
label_desc: Use image node_id 21 for calling the IndependentDistributionTest obj's underlying member method code with example usage: obj.testChangingVariableShapes() without return types