code: def approx_expected_softplus(
      m, s, alpha=DEFAULT_MIX_PROB, c=DEFAULT_SCALE, name=None
  ):
      """Approximates `E_{N(m,s)}[softplus(X)]`."""
      with tf.name_scope(name or "approx_expected_softplus"):
          (
              alpha,
              m_over_rho,
              cdf,
              pdf,
              one_over_rho,
              m_,
              s_,
              c,
          ) = _common(m, s, alpha, c)
          return tf.math.reduce_sum(
              alpha
              * (
                  (c**2 + s_**2) * one_over_rho * pdf(m_over_rho)
                  + m_ * 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 6 for calling a global function with example usage: approx_expected_softplus(m, s, alpha, c, name) and returns: tf