code: def report(self, report_pep=False):
      snapshot_total = timeit.default_timer() - self.start
  
      def path(key):
          return " -> ".join(label for label, _ in key)
  
      def print_pep(results, snapshot_total):
          for key, times in sorted(self.times.items()):
              if path(key) == "evaluate -> pytorch eval once":
                  info = {
                      "type": path(key),
                      "metric": "latency",
                      "unit": "ms",
                      "value": f"{times.average * 1000:.1f}",
                  }
                  print("PyTorchObserver " + json_dumps(info))
  
      if len(self.times) == 0:
          print(
              "Note: Nothing was reported. "
              'Please use timing.time("foo") to measure time.'
          )
          return
  
      results = [
          {
              "name": path(key),
              "total": format_time(times.sum),
              "avg": format_time(times.average),
              "max": format_time(times.max),
              "p50": format_time(times.p50),
              "p90": format_time(times.p90),
              "p99": format_time(times.p99),
              "count": times.count,
          }
          for key, times in sorted(self.times.items())
      ]
      print(
          ascii_table(
              results,
              human_column_names={
                  "name": "Stage",
                  "total": "Total",
                  "avg": "Average",
                  "max": "Max",
                  "p50": "P50",
                  "p90": "P90",
                  "p99": "P99",
                  "count": "Count",
              },
              footer={
                  "name": "Total time",
                  "total": format_time(snapshot_total),
              },
              alignments={"name": "<"},
          )
      )
      if report_pep:
          print_pep(results, snapshot_total)
  
imports: import collections
  import functools
  import timeit
  import traceback
  import weakref
  from json import dumps
  from typing import List
  import numpy
  from .ascii_table import ascii_table
label_desc: Use image node_id 2 for calling the Snapshot obj's underlying member method code with example usage: obj.report(report_pep) and returns: str