def autosummary(name: str, value: TfExpressionEx, passthru: TfExpressionEx = None, condition: TfExpressionEx = True) -> TfExpressionEx:
    """Create a new autosummary.

    Args:
        name:     Name to use in TensorBoard
        value:    TensorFlow expression or python value to track
        passthru: Optionally return this TF node without modifications but tack an autosummary update side-effect to this node.

    Example use of the passthru mechanism:

    n = autosummary('l2loss', loss, passthru=n)

    This is a shorthand for the following code:

    with tf.control_dependencies([autosummary('l2loss', loss)]):
        n = tf.identity(n)
    """
    tfutil.assert_tf_initialized()
    name_id = name.replace("/", "_")

    if tfutil.is_tf_expression(value):
        with tf.name_scope("summary_" + name_id), tf.device(value.device):
            condition = tf.convert_to_tensor(condition, name='condition')
            update_op = tf.cond(condition, lambda: tf.group(_create_var(name, value)), tf.no_op)
            with tf.control_dependencies([update_op]):
                return tf.identity(value if passthru is None else passthru)

    else:  # python scalar or numpy array
        assert not tfutil.is_tf_expression(passthru)
        assert not tfutil.is_tf_expression(condition)
        if condition:
            if name not in _immediate:
                with tfutil.absolute_name_scope("Autosummary/" + name_id), tf.device(None), tf.control_dependencies(None):
                    update_value = tf.placeholder(_dtype)
                    update_op = _create_var(name, update_value)
                    _immediate[name] = update_op, update_value
            update_op, update_value = _immediate[name]
            tfutil.run(update_op, {update_value: value})
        return value if passthru is None else passthru
Create a new autosummary.

    Args:
        name:     Name to use in TensorBoard
        value:    TensorFlow expression or python value to track
        passthru: Optionally return this TF node without modifications but tack an autosummary update side-effect to this node.

    Example use of the passthru mechanism:

    n = autosummary('l2loss', loss, passthru=n)

    This is a shorthand for the following code:

    with tf.control_dependencies([autosummary('l2loss', loss)]):
        n = tf.identity(n)
    
converters/stylegan2ada_tf_official/dnnlib/tflib/autosummary.py