class_docstr: Wrapper to run an algorithm with multiple dataset sizes
  and compute speedup of cuml relative to sklearn baseline.
class_docstr_tok: ["Wrapper","to","run","an","algorithm","with","multiple","dataset","sizes","and","compute","speedup","of","cuml","relative","to","sklearn","baseline","."]
code: def run(
      self,
      algo_pair,
      param_overrides={},
      cuml_param_overrides={},
      cpu_param_overrides={},
      dataset_param_overrides={},
      dtype=np.float32,
      *,
      run_cpu=True,
      device="gpu",
      raise_on_error=False,
      verbose=False,
  ):
      all_results = []
      for ns in self.bench_rows:
          for nf in self.bench_dims:
              try:
                  all_results.append(
                      self._run_one_size(
                          algo_pair,
                          ns,
                          nf,
                          param_overrides,
                          cuml_param_overrides=cuml_param_overrides,
                          cpu_param_overrides=cpu_param_overrides,
                          dataset_param_overrides=dataset_param_overrides,
                          dtype=dtype,
                          run_cpu=run_cpu,
                          device=device,
                          verbose=verbose,
                      )
                  )
              except Exception as e:
                  print(
                      "Failed to run with %d samples, %d features: %s"
                      % (ns, nf, str(e))
                  )
                  if raise_on_error:
                      raise
                  all_results.append(dict(n_samples=ns, n_features=nf))
      return all_results
  
file_path: cuml/python/cuml/benchmark/runners.py
imports: from cuml.internals.safe_imports import gpu_only_import_from
  from cuml.benchmark import datagen
  from cuml.common.device_selection import using_device_type
  import warnings
  import time
  import itertools
  from cuml.internals.safe_imports import cpu_only_import
label_desc: Use image node_id 3 for calling the SpeedupComparisonRunner obj's underlying member method code with example usage: obj.run(algo_pair, param_overrides, cuml_param_overrides, cpu_param_overrides, dataset_param_overrides, dtype) and returns: all_results