class_docstr: Context manager that generates a step trace event in the profiler.
  
  The step trace event spans the duration of the code enclosed by the context.
  The profiler will provide the performance analysis for each step trace event.
  
  For example, it can be used to mark training steps and enable the profiler to
  provide the performance analysis per step:
  
  >>> while global_step < NUM_STEPS:                                           # doctest: +SKIP
  ...   with jax.profiler.StepTraceAnnotation("train", step_num=global_step):  # doctest: +SKIP
  ...     train_step()                                                         # doctest: +SKIP
  ...     global_step += 1                                                     # doctest: +SKIP
  
  This will cause a "train xx" event to show up on the trace timeline if the
  event occurs while the process is being traced by TensorBoard. In addition,
  if using accelerators, the device trace timeline will also show a "train xx"
  event. Note that "step_num" can be set as a keyword argument to pass the
  global step number to the profiler.
code: def __init__(self, name: str, **kwargs):
      super().__init__(name, _r=1, **kwargs)
  
imports: from __future__ import annotations
  from contextlib import contextmanager
  from functools import wraps
  import glob
  import gzip
  import http.server
  import json
  import logging
  import os
  import socketserver
  import threading
  from typing import Callable
  from jax._src import traceback_util
  from jax._src import xla_bridge
  from jax._src.lib import xla_client
label_desc: Use image node_id 1 to create a new StepTraceAnnotation object from inherited base classes: TraceAnnotation with example: obj = StepTraceAnnotation(name)