code: def astype(self, dtype=None, copy=True):
          
          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)
docs: 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
          --------
          >>> arr = SparseArray([0, 0, 1, 2])
          >>> arr
          [0, 0, 1, 2]
          Fill: 0
          IntIndex
          Indices: array([2, 3], dtype=int32)
  
          >>> arr.astype(np.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``.
  
          >>> arr.astype(np.dtype('float64'))
          ... # doctest: +NORMALIZE_WHITESPACE
          [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.
  
          >>> arr.astype(SparseDtype("float64", fill_value=np.nan))
          ... # doctest: +NORMALIZE_WHITESPACE
          [nan, nan, 1.0, 2.0]
          Fill: nan
          IntIndex
          Indices: array([2, 3], dtype=int32)