code: def approx_variance_sigmoid(
      m, s, alpha=DEFAULT_MIX_PROB, c=DEFAULT_SCALE, name=None
  ):
      """Approxmates `Var_{N(m,s)}[sigmoid(X)]`."""
      # TODO(jvdillon): See if we can rederive things so we can avoid catastrophic
      # cancellation which might be present in the following calculation. One idea
      # might be to apply the law of total variance by leveraging the fact that each
      # of the calculations below are subdivided into three segments.
      with tf.name_scope(name or "approx_variance_sigmoid"):
          alpha, m_over_rho, cdf, _, _, _, s_, c = _common(
              m, s, alpha, c
          )
          c2 = c**2
          c2s2_ = c2 * s_**2
          c2_over_big_rho_ = c2[:, tf.newaxis] * tf.math.rsqrt(
              c2[tf.newaxis, :] * c2[:, tf.newaxis]
              + c2s2_[..., tf.newaxis, :]
              + c2s2_[..., :, tf.newaxis]
          )
          m_over_rho_ = m_over_rho[..., tf.newaxis]
          b = 0.5 * cdf(m_over_rho_) - special.owens_t(
              m_over_rho_, c2_over_big_rho_
          )
          bt = tf.linalg.matrix_transpose(b)
          mom2 = tf.math.reduce_sum(
              alpha[tf.newaxis, :] * alpha[:, tf.newaxis] * (b + bt),
              axis=[-2, -1],
          )
          return mom2 - approx_expected_sigmoid(m, s, alpha, c) ** 2.0
  
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 8 for calling a global function with example usage: approx_variance_sigmoid(m, s, alpha, c, name) and returns: unknown