code: def pdf(x, probs):
      # do not evaluate close to 0.5
      x = np.array(x, dtype=np.float32)
      probs = np.array(probs, dtype=np.float32)
      return (
          np.power(probs, x)
          * np.power(1.0 - probs, 1.0 - x)
          * np.exp(log_norm_const(probs))
      )
  
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 3 for calling a global function with example usage: pdf(x, probs) and returns: unknown