code: def main():
      df = pd.read_csv(_DATA.value).sample(_NUM_SAMPLES.value)
      shared_stream = torch.cuda.Stream()
      measured_times = []
      pbar = tqdm.tqdm(total=_NUM_SAMPLES.value, ncols=0)
      with torch.cuda.stream(shared_stream):
          for _, r in df.iterrows():
              measured_times.append(
                  get_actual_time(r, shared_stream, pbar)
              )
      df = df.assign(measured_min_time_ms=measured_times)
      pbar.close()
  
      def absolute_error(r):
          return abs(r.measured_min_time_ms - r.min_time_ms)
  
      def relative_error(r):
          return absolute_error(r) / r.min_time_ms
  
      errors = df.assign(absolute_error=absolute_error).assign(
          relative_error=relative_error
      )[["absolute_error", "relative_error"]]
      print(errors)
      print(errors.describe())
      df.to_csv(_OUTPUT_FILE.value)
  
file_path: tensorflow-upstream/third_party/xla/xla/experiments/triton_autotuning/check_csv.py
imports: import sys
  from absl import app
  from absl import flags
  from matmul_lib import benchmark_matmul
  from matmul_lib import MatmulSize
  from matmul_lib import MatmulTiling
  from matmul_lib import MatrixLayout
  from matmul_lib import QuantizedInputType
  import pandas
  import torch
  import tqdm
label_desc: Use image node_id 2 for calling a global function with example usage: main() and returns: abs, unknown