human
Solve the following python programming problem given the following code:
def apply(self, func, convert_dtype = True, args = (), **kwds) : 
    "
        Invoke function on values of Series. Can be ufunc (a NumPy function
        that applies to the entire Series) or a Python function that only works
        on single values

        Parameters
        ----------
        func : function
        convert_dtype : boolean, default True
            Try to find better dtype for elementwise function results. If
            False, leave as dtype=object
        args : tuple
            Positional arguments to pass to function in addition to the value
        Additional keyword arguments will be passed as keywords to the function

        Returns
        -------
        y : Series or DataFrame if func returns a Series

        See also
        --------
        Series.map: For element-wise operations

        Examples
        --------

        Create a series with typical summer temperatures for each city.

        >>> import pandas as pd
        >>> import numpy as np
        >>> series = pd.Series([20, 21, 12], index=['London',
        ... 'New York','Helsinki'])
        London      20
        New York    21
        Helsinki    12
        dtype: int64

        Square the values by defining a function and passing it as an
        argument to ``apply()``.

        >>> def square(x):
        ...     return x**2
        >>> series.apply(square)
        London      400
        New York    441
        Helsinki    144
        dtype: int64

        Square the values by passing an anonymous function as an
        argument to ``apply()``.

        >>> series.apply(lambda x: x**2)
        London      400
        New York    441
        Helsinki    144
        dtype: int64

        Define a custom function that needs additional positional
        arguments and pass these additional arguments using the
        ``args`` keyword.

        >>> def subtract_custom_value(x, custom_value):
        ...     return x-custom_value

        >>> series.apply(subtract_custom_value, args=(5,))
        London     ...

    
    

    Fix the buggy line: if isinstance (convert_dtype, np.ufunc) :
assistant
#Here is Python program to find better dtype for elementwise function results.

if isinstance (f, np.ufunc) :