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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 astype(self, dtype=None, copy=True):
 """ Change the dtype of a SparseArray. The output will always be a SparseArray. To convert to a dense ndarray with a certain dtype, use :meth:`numpy.asarray`. Parameters dtype : np.dtype or ExtensionDtype For SparseDtype, this changes the dtype of ``self.sp_values`` and the ``self.fill_value``. For other dtypes, this only changes the dtype of ``self.sp_values``. copy : bool, default True Whether to ensure a copy is made, even if not necessary. Returns ------- SparseArray Examples -------- [0, 0, 1, 2] Fill: 0 IntIndex Indices: array([2, 3], dtype=int32) [0, 0, 1, 2] Fill: 0 IntIndex Indices: array([2, 3], dtype=int32) Using a NumPy dtype with a different kind (e.g. float) will coerce just ``self.sp_values``. [0, 0, 1.0, 2.0] Fill: 0 IntIndex Indices: array([2, 3], dtype=int32) Use a SparseDtype if you wish to be change the fill value as well. [nan, nan, 1.0, 2.0] Fill: nan IntIndex Indices: array([2, 3], dtype=int32) """

        dtype = self.dtype.update_dtype(dtype)
        subtype = dtype._subtype_with_str
        sp_values = astype_nansafe(self.sp_values,
                                   subtype,
                                   copy=copy)
        if sp_values is self.sp_values and copy:
            sp_values = sp_values.copy()

        return self._simple_new(sp_values,
                                self.sp_index,
                                dtype)