class_docstr: Manage profile data for each run, each run may have multiple workers and spans.
  We should manage profile data of each (worker, span) unit.
  Besides, a special worker "all" is created to merge all profile data for distributed view.
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docstr: Return all spans in current run data.
  spans: Collecting profile data when training your model can be divided into several parts supported by            paddle.profiler api, for example,  you may profile steps 2-4, 6-8. Each range is called a span here.             And We index each span by orders.
docstr_tok: ["Return","all","spans","in","current","run","data",".","spans",":","Collecting","profile","data","when","training","your","model","can","be","divided","into","several","parts","supported","by","paddle.profiler","api",",","for","example",",","you","may","profile","steps","2-4",",","6-8",".","Each","range","is","called","a","span","here",".","And","We","index","each","span","by","orders","."]
code: def get_spans(self, worker_name):
      """
          Return all spans in current run data.
          spans: Collecting profile data when training your model can be divided into several parts supported by\
              paddle.profiler api, for example,  you may profile steps 2-4, 6-8. Each range is called a span here. \
              And We index each span by orders.
          """
      spans = list(self.profiler_data[worker_name].keys())
      spans = sorted([int(span) for span in spans])
      spans = [str(span) for span in spans]
      return spans
  
file_path: VisualDL/visualdl/component/profiler/run_manager.py
imports: from collections import defaultdict
  from threading import Thread
  from .profiler_data import DistributedProfilerData
  from .profiler_data import ProfilerData
label_desc: Use image node_id 6 for calling the RunManager obj's underlying member method code with example usage: obj.get_spans(worker_name) and returns: spans