code: def _power(support, n):
      """Construct n-fold cartesian product of a tensor with itself."""
      return _cartesian_product(*(n * [tf.expand_dims(support, -1)]))
  
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import bernoulli
  from tensorflow_probability.python.distributions import categorical
  from tensorflow_probability.python.distributions import joint_distribution
  from tensorflow_probability.python.distributions import joint_distribution_coroutine
  from tensorflow_probability.python.distributions import sample
  from tensorflow_probability.python.experimental.marginalize.logeinsumexp import logeinsumexp
  from tensorflow_probability.python.internal import prefer_static
  from tensorflow_probability.python.internal import samplers
label_desc: Use image node_id 3 for calling a global function with example usage: _power(support, n) and returns: _cartesian_product