Run benchmarks for module using nose.
| Parameters : | label : {‘fast’, ‘full’, ‘’, attribute identifier}, optional 
 verbose : int, optional 
 extra_argv : list, optional 
  | 
|---|---|
| Returns : | success : bool 
  | 
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() 
Running benchmarks for numpy.lib
...
using 562341 items:
unique:
0.11
unique1d:
0.11
ratio: 1.0
nUnique: 56230 == 56230
...
OK
>>> success 
True