code: def optimize(*, key=None, path=None, readonly=False, **config_dict):
      """Context manager that optimizes kernel launch parameters.
  
      In this context, CuPy's routines find the best kernel launch parameter
      values (e.g., the number of threads and blocks). The found values are
      cached and reused with keys as the shapes, strides and dtypes of the
      given inputs arrays.
  
      Args:
          key (string or None): The cache key of optimizations.
          path (string or None): The path to save optimization cache records.
              When path is specified and exists, records will be loaded from
              the path. When readonly option is set to ``False``, optimization
              cache records will be saved to the path after the optimization.
          readonly (bool): See the description of ``path`` option.
          max_trials (int): The number of trials that defaults to 100.
          timeout (float):
              Stops study after the given number of seconds. Default is 1.
          max_total_time_per_trial (float):
              Repeats measuring the execution time of the routine for the
              given number of seconds. Default is 0.1.
  
      Examples
      --------
      >>> import cupy
      >>> from cupyx import optimizing
      >>>
      >>> x = cupy.arange(100)
      >>> with optimizing.optimize():
      ...     cupy.sum(x)
      ...
      array(4950)
  
      .. note::
        Optuna (https://optuna.org) installation is required.
        Currently it works for reduction operations only.
      """
      if not _optuna_available:
          raise RuntimeError(
              "Optuna is required to run optimization. "
              "See https://optuna.org/ for the installation instructions."
          )
  
      old_context = _optimize_config.get_current_context()
      context = _optimize_config.get_new_context(
          key, _optimize, config_dict
      )
      _optimize_config.set_current_context(context)
  
      if path is not None:
          if os.path.exists(path):
              context.load(path)
          elif readonly:
              warnings.warn(
                  """
  The specified path {} could not be found, and the readonly option is set.
  The optimization results will never be stored.
  """.format(
                      path
                  )
              )
  
      try:
          yield context
          if path is not None and not readonly:
              if context._is_dirty() or not os.path.exists(path):
                  context.save(path)
      finally:
          _optimize_config.set_current_context(old_context)
  
imports: import contextlib
  import math
  import os
  import warnings
  from cupy._core import _optimize_config
  from cupyx import profiler
label_desc: Use image node_id 2 for calling a global function with example usage: optimize() without return types