def bench(self, label='<STR_LIT>', verbose=<NUM_LIT:1>, extra_argv=None):
print("<STR_LIT>" % self.package_name)<EOL>self._show_system_info()<EOL>argv = self._test_argv(label, verbose, extra_argv)<EOL>argv += ['<STR_LIT>', r'<STR_LIT>' % os.sep]<EOL>nose = import_nose()<EOL>from .noseclasses import Unplugger<EOL>add_plugins = [Unplugger('<STR_LIT>')]<EOL>return nose.run(argv=argv, addplugins=add_plugins)<EOL>
Run benchmarks for module using nose.

Parameters
----------
label : {'fast', 'full', '', attribute identifier}, optional
    Identifies the benchmarks to run. This can be a string to pass to
    the nosetests executable with the '-A' option, or one of several
    special values.  Special values are:
    * 'fast' - the default - which corresponds to the ``nosetests -A``
      option of 'not slow'.
    * 'full' - fast (as above) and slow benchmarks as in the
      'no -A' option to nosetests - this is the same as ''.
    * None or '' - run all tests.
    attribute_identifier - string passed directly to nosetests as '-A'.
verbose : int, optional
    Verbosity value for benchmark outputs, in the range 1-10. Default is 1.
extra_argv : list, optional
    List with any extra arguments to pass to nosetests.

Returns
-------
success : bool
    Returns True if running the benchmarks works, False if an error
    occurred.

Notes
-----
Benchmarks are like tests, but have names starting with "bench" instead
of "test", and can be found under the "benchmarks" sub-directory of the
module.

Each NumPy module exposes `bench` in its namespace to run all benchmarks
for it.

Examples
--------
>>> success = np.lib.bench() #doctest: +SKIP
Running benchmarks for numpy.lib
...
using 562341 items:
unique:
0.11
unique1d:
0.11
ratio: 1.0
nUnique: 56230 == 56230
...
OK

>>> success #doctest: +SKIP
True