code: def make_continuous_bernoulli(batch_shape, dtype=tf.float32):
      prob = np.random.uniform(size=list(batch_shape))
      prob = tf.constant(prob, dtype=tf.float32)
      return continuous_bernoulli.ContinuousBernoulli(
          probs=prob, dtype=dtype, validate_args=True
      )
  
imports: import numpy
  from scipy import special
  import tensorflow.compat.v1
  import tensorflow.compat.v2
  from tensorflow_probability.python.distributions import continuous_bernoulli
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.internal import tensorshape_util
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 1 for calling a global function with example usage: make_continuous_bernoulli(batch_shape, dtype) and returns: continuous_bernoulli