def astype(self, dtype, copy=True):
        """Returns a copy of the array after casting to a specified type.

        Parameters
        ----------
        dtype : numpy.dtype or str
            The type of the returned array.
        copy : bool
            Default `True`. By default, astype always returns a newly
            allocated ndarray on the same context. If this is set to
            `False`, and the dtype requested is the same as the ndarray's
            dtype, the ndarray is returned instead of a copy.

        Returns
        -------
        NDArray, CSRNDArray or RowSparseNDArray
            The copied array after casting to the specified type, or
            the same array if copy=False and dtype is the same as the input
            array.

        Examples
        --------
        >>> x = mx.nd.zeros((2,3), dtype='float32')
        >>> y = x.astype('int32')
        >>> y.dtype
        <type 'numpy.int32'>
        """

        if not copy and np.dtype(dtype) == self.dtype:
            return self

        res = empty(self.shape, ctx=self.context, dtype=dtype)
        self.copyto(res)
        return res
def astype ( self , dtype , copy = True ) : if not copy and np . dtype ( dtype ) == self . dtype : return self res = empty ( self . shape , ctx = self . context , dtype = dtype ) self . copyto ( res ) return res
Returns a copy of the array after casting to a specified type.

        Parameters
        ----------
        dtype : numpy.dtype or str
            The type of the returned array.
        copy : bool
            Default `True`. By default, astype always returns a newly
            allocated ndarray on the same context. If this is set to
            `False`, and the dtype requested is the same as the ndarray's
            dtype, the ndarray is returned instead of a copy.

        Returns
        -------
        NDArray, CSRNDArray or RowSparseNDArray
            The copied array after casting to the specified type, or
            the same array if copy=False and dtype is the same as the input
            array.

        Examples
        --------
        >>> x = mx.nd.zeros((2,3), dtype='float32')
        >>> y = x.astype('int32')
        >>> y.dtype
        <type 'numpy.int32'>