code: def process_results(results, verbose=False):
      baseline = results.best_baseline()
  
      def like_baseline(x):
          for key in (
              "n_iter",
              "batch_size",
              "l2",
              "learning_rate",
              "loss",
              "embedding_dim",
          ):
              if x[key] != baseline[key]:
                  return False
  
          return True
  
      data = pd.DataFrame([x for x in results if like_baseline(x)])
  
      best = (
          data.sort_values("test_mrr", ascending=False)
          .groupby("compression_ratio", as_index=False)
          .first()
      )
  
      # Normalize per iteration
      best["elapsed"] = best["elapsed"] / best["n_iter"]
  
      if verbose:
          print(best)
  
      baseline_mrr = best[best["compression_ratio"] == 1.0][
          "validation_mrr"
      ].values[0]
      baseline_time = best[best["compression_ratio"] == 1.0][
          "elapsed"
      ].values[0]
  
      compression_ratio = best["compression_ratio"].values
      mrr = best["validation_mrr"].values / baseline_mrr
      elapsed = best["elapsed"].values / baseline_time
  
      return compression_ratio[:-1], mrr[:-1], elapsed[:-1]
  
imports: import argparse
  import pandas
  import matplotlib
  import matplotlib.pyplot
  import seaborn
  from example import Results
label_desc: Use image node_id 1 for calling a global function with example usage: process_results(results, verbose) and returns: compression_ratio,  mrr,  elapsed, True, False