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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def to_numpy(self, dtype=None, copy=False):
 """ Convert the DataFrame to a NumPy array. .. versionadded:: 0.24.0 By default, the dtype of the returned array will be the common NumPy dtype of all types in the DataFrame. For example, if the dtypes are ``float16`` and ``float32``, the results dtype will be ``float32``. This may require copying data and coercing values, which may be expensive. Parameters dtype : str or numpy.dtype, optional The dtype to pass to :meth:`numpy.asarray` copy : bool, default False Whether to ensure that the returned value is a not a view on another array. Note that ``copy=False`` does not *ensure* that ``to_numpy()`` is no-copy. Rather, ``copy=True`` ensure that a copy is made, even if not strictly necessary. Returns ------- numpy.ndarray See Also -------- Series.to_numpy : Similar method for Series. Examples -------- array([[1, 3], [2, 4]]) With heterogenous data, the lowest common type will have to be used. array([[1. , 3. ], [2. , 4.5]]) For a mix of numeric and non-numeric types, the output array will have object dtype. array([[1, 3.0, Timestamp('2000-01-01 00:00:00')], [2, 4.5, Timestamp('2000-01-02 00:00:00')]], dtype=object) """

        result = np.array(self.values, dtype=dtype, copy=copy)
        return result