def jeffreys(logu, name=None):
with tf.compat.v1.name_scope(name, "<STR_LIT>", [logu]):<EOL><INDENT>logu = tf.convert_to_tensor(value=logu, name="<STR_LIT>")<EOL>return <NUM_LIT:0.5> * tf.math.expm1(logu) * logu<EOL><DEDENT>
The Jeffreys Csiszar-function in log-space.

    A Csiszar-function is a member of,

    ```none
    F = { f:R_+ to R : f convex }.
    ```

    The Jeffreys Csiszar-function is:

    ```none
    f(u) = 0.5 ( u log(u) - log(u) )
         = 0.5 kl_forward + 0.5 kl_reverse
         = symmetrized_csiszar_function(kl_reverse)
         = symmetrized_csiszar_function(kl_forward)
    ```

    This Csiszar-function induces a symmetric f-Divergence, i.e.,
    `D_f[p, q] = D_f[q, p]`.

    Warning: this function makes non-log-space calculations and may therefore be
    numerically unstable for `|logu| >> 0`.

    Args:
      logu: `float`-like `Tensor` representing `log(u)` from above.
      name: Python `str` name prefixed to Ops created by this function.

    Returns:
      jeffreys_of_u: `float`-like `Tensor` of the Csiszar-function evaluated
        at `u = exp(logu)`.