def _finish_prob_for_one_fiber(self, y, x, ildj, event_ndims,
                                 **distribution_kwargs):
    """Finish computation of prob on one element of the inverse image."""
    x = self._maybe_rotate_dims(x, rotate_right=True)
    prob = self.distribution.prob(x, **distribution_kwargs)
    if self._is_maybe_event_override:
      prob = tf.reduce_prod(input_tensor=prob, axis=self._reduce_event_indices)
    prob *= tf.exp(tf.cast(ildj, prob.dtype))
    if self._is_maybe_event_override and isinstance(event_ndims, int):
      tensorshape_util.set_shape(
          prob,
          tf.broadcast_static_shape(
              tensorshape_util.with_rank_at_least(y.shape, 1)[:-event_ndims],
              self.batch_shape))
    return prob
def _finish_prob_for_one_fiber ( self , y , x , ildj , event_ndims , ** distribution_kwargs ) : x = self . _maybe_rotate_dims ( x , rotate_right = True ) prob = self . distribution . prob ( x , ** distribution_kwargs ) if self . _is_maybe_event_override : prob = tf . reduce_prod ( input_tensor = prob , axis = self . _reduce_event_indices ) prob *= tf . exp ( tf . cast ( ildj , prob . dtype ) ) if self . _is_maybe_event_override and isinstance ( event_ndims , int ) : tensorshape_util . set_shape ( prob , tf . broadcast_static_shape ( tensorshape_util . with_rank_at_least ( y . shape , 1 ) [ : - event_ndims ] , self . batch_shape ) ) return prob
Finish computation of prob on one element of the inverse image.