code: def approx_expected_sigmoid(
      m, s, alpha=DEFAULT_MIX_PROB, c=DEFAULT_SCALE, name=None
  ):
      """Approximates `E_{N(m,s)}[sigmoid(X)]`."""
      with tf.name_scope(name or "approx_expected_sigmoid"):
          alpha, m_over_rho, cdf = _common(m, s, alpha, c)[:3]
          return tf.math.reduce_sum(alpha * cdf(m_over_rho), axis=-1)
  
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 7 for calling a global function with example usage: approx_expected_sigmoid(m, s, alpha, c, name) and returns: tf