tensorflow_probability/python/distributions/sample.py
def _make_summary_statistic(attr):
  """Factory for implementing summary statistics, eg, mean, stddev, mode."""
  def _fn(self, **kwargs):
    """Implements summary statistic, eg, mean, stddev, mode."""
    x = getattr(self.distribution, attr)(**kwargs)
    shape = prefer_static.concat([
        self.distribution.batch_shape_tensor(),
        prefer_static.ones(prefer_static.rank_from_shape(self.sample_shape),
                           dtype=self.sample_shape.dtype),
        self.distribution.event_shape_tensor(),
    ], axis=0)
    x = tf.reshape(x, shape=shape)
    shape = prefer_static.concat([
        self.distribution.batch_shape_tensor(),
        self.sample_shape,
        self.distribution.event_shape_tensor(),
    ], axis=0)
    return tf.broadcast_to(x, shape)
  return _fn
def _make_summary_statistic(attr):
  """Factory for implementing summary statistics, eg, mean, stddev, mode."""
  def _fn(self, **kwargs):
    """Implements summary statistic, eg, mean, stddev, mode."""
    x = getattr(self.distribution, attr)(**kwargs)
    shape = prefer_static.concat([
        self.distribution.batch_shape_tensor(),
        prefer_static.ones(prefer_static.rank_from_shape(self.sample_shape),
                           dtype=self.sample_shape.dtype),
        self.distribution.event_shape_tensor(),
    ], axis=0)
    x = tf.reshape(x, shape=shape)
    shape = prefer_static.concat([
        self.distribution.batch_shape_tensor(),
        self.sample_shape,
        self.distribution.event_shape_tensor(),
    ], axis=0)
    return tf.broadcast_to(x, shape)
  return _fn
Factory for implementing summary statistics, eg, mean, stddev, mode.