code: def approx_expected_log_prob_sigmoid(
      m, s, y=None, alpha=DEFAULT_MIX_PROB, c=DEFAULT_SCALE, name=None
  ):
      """Approximates `E_{N(m,s)}[Bernoulli(sigmoid(X)).log_prob(Y)]`."""
      with tf.name_scope(name or "approx_expected_log_prob_sigmoid"):
          m, s, alpha, c = _prepare_args(m, s, alpha, c)
          ym = m if y is None else tf.cast(y, m.dtype, name="y") * m
          return ym - approx_expected_softplus(m, s, alpha, c)
  
imports: import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import sigmoid
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.distributions import kullback_leibler
  from tensorflow_probability.python.distributions import normal
  from tensorflow_probability.python.distributions import transformed_distribution
  from tensorflow_probability.python.internal import assert_util
  from tensorflow_probability.python.internal import dtype_util
  from tensorflow_probability.python.internal import parameter_properties
  from tensorflow_probability.python.math import special
label_desc: Use image node_id 5 for calling a global function with example usage: approx_expected_log_prob_sigmoid(m, s, y, alpha, c, name) and returns: unknown