if s.g.m != nil {
					ug.mid = int64(s.g.m.procid)
				}
				ug.status = readgstatus(s.g) &^ _Gscan
				ug.waitreason = s.g.waitreason
				ug.inMarkAssist = s.g.inMarkAssist
				ug.stackID = traceStack(0, gp, gen)
			}
			resumeG(s)
			casgstatus(me, _Gwaiting, _Grunning)
		})
		if ug.goid != 0 {
			untracedGs = append(untracedGs, ug)
		}
	})

	if !stopTrace {

gives module error.
For example,

Example 1:

>>> a = [1., 2., 3.]
>>> equality_bitcast = tf.bitcast(a, tf.complex128)
Traceback (most recent call last):
...
InvalidArgumentError: Cannot bitcast from 1 to 18 [Op:Bitcast]
>>> equality_cast = tf.cast(a, tf.complex128)
>>> print(equality_cast)

    `tf.debugging.enable_traceback_filtering()`. If you are debugging a
    TensorFlow-internal issue (e.g. to prepare a TensorFlow PR), make sure to
    disable traceback filtering. You can check whether this feature is currently
    enabled by calling `tf.debugging.is_traceback_filtering_enabled()`.

    Note that this feature is only available with Python 3.7 or higher.

	startTime := time.Now()
	return func(info objTraceInfoer, err error) {
		duration := time.Since(startTime)
		if globalTrace.NumSubscribers(madmin.TraceBatch) > 0 {
			globalTrace.Publish(batchJobTrace(d, job, startTime, duration, info, attempts, err))
			return
		}
		switch d {
		case batchJobMetricReplication:

    ```
    """

    def __init__(
        self: AppType,
        *,
        debug: Annotated[
            bool,
            Doc(
                """
                Boolean indicating if debug tracebacks should be returned on server
                errors.

                Read more in the
                [Starlette docs for Applications](https://www.starlette.io/applications/#instantiating-the-application).