code: def cdf(x, probs):
      # do not evaluate close to 0.5
      tentative_cdf = sp_special.xlogy(x, probs) + sp_special.xlog1py(
          1 - x, -probs
      )
      tentative_cdf = (np.expm1(tentative_cdf) + probs) / (
          2 * probs - 1.0
      )
      correct_above = np.where(x > 1.0, 1.0, tentative_cdf)
      return np.where(x < 0, 0.0, correct_above)
  
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 7 for calling a global function with example usage: cdf(x, probs) and returns: np