code: def make_bernoulli(batch_shape, dtype=tf.int32):
      p = np.random.uniform(size=list(batch_shape))
      p = tf.constant(p, dtype=tf.float32)
      return probit_bernoulli.ProbitBernoulli(
          probs=p, 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 kullback_leibler
  from tensorflow_probability.python.distributions import probit_bernoulli
  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_bernoulli(batch_shape, dtype) and returns: probit_bernoulli