code: def _measure_speed(self, func) -> float:
      try:
          if self.args.is_tpu or self.args.torchscript:
              # run additional 10 times to stabilize compilation for tpu and torchscript
              logger.info(
                  "Do inference on TPU or torchscript. Running model 5 times to stabilize compilation"
              )
              timeit.repeat(
                  func,
                  repeat=1,
                  number=5,
              )
  
          # as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
          runtimes = timeit.repeat(
              func,
              repeat=self.args.repeat,
              number=10,
          )
  
          if self.args.is_tpu and self.args.torch_xla_tpu_print_metrics:
              import torch_xla.debug.metrics as met
  
              self.print_fn(met.metrics_report())
  
          return min(runtimes) / 10.0
      except RuntimeError as e:
          self.print_fn(f"Doesn't fit on GPU. {e}")
          return "N/A"
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/benchmark/benchmark.py
imports: import timeit
  from typing import Callable, Optional
  from ..configuration_utils import PretrainedConfig
  from ..models.auto.modeling_auto import MODEL_MAPPING, MODEL_WITH_LM_HEAD_MAPPING
  from ..utils import is_py3nvml_available, is_torch_available, logging
  from .benchmark_utils import Benchmark, Memory, MemorySummary, measure_peak_memory_cpu, start_memory_tracing, stop_memory_tracing
label_desc: Use image node_id 8 for calling the PyTorchBenchmark obj's underlying member method code with example usage: obj._measure_speed(func) and returns: unknown, str