parameters: (self, repeat=default_repeat, number=default_number)
return_statement: return r
docstring: Call timeit() a few times.
  
          This is a convenience function that calls the timeit()
          repeatedly, returning a list of results.  The first argument
          specifies how many times to call timeit(), defaulting to 3;
          the second argument specifies the timer argument, defaulting
          to one million.
  
          Note: it's tempting to calculate mean and standard deviation
          from the result vector and report these.  However, this is not
          very useful.  In a typical case, the lowest value gives a
          lower bound for how fast your machine can run the given code
          snippet; higher values in the result vector are typically not
          caused by variability in Python's speed, but by other
          processes interfering with your timing accuracy.  So the min()
          of the result is probably the only number you should be
          interested in.  After that, you should look at the entire
          vector and apply common sense rather than statistics.
docstring_summary: Call timeit() a few times.
docstring_tokens: Call timeit () a few times .
function: def repeat(self, repeat=default_repeat, number=default_number):
          """Call timeit() a few times.
  
          This is a convenience function that calls the timeit()
          repeatedly, returning a list of results.  The first argument
          specifies how many times to call timeit(), defaulting to 3;
          the second argument specifies the timer argument, defaulting
          to one million.
  
          Note: it's tempting to calculate mean and standard deviation
          from the result vector and report these.  However, this is not
          very useful.  In a typical case, the lowest value gives a
          lower bound for how fast your machine can run the given code
          snippet; higher values in the result vector are typically not
          caused by variability in Python's speed, but by other
          processes interfering with your timing accuracy.  So the min()
          of the result is probably the only number you should be
          interested in.  After that, you should look at the entire
          vector and apply common sense rather than statistics.
          """
          r = []
          for i in range(repeat):
              t = self.timeit(number)
              r.append(t)
          return r
function_tokens: def repeat ( self , repeat = default_repeat , number = default_number ) : r = [ ] for i in range ( repeat ) : t = self . timeit ( number ) r . append ( t ) return r