<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def copy(self, deep=True, data=None):
 """Returns a copy of this array. If `deep=True`, a deep copy is made of the data array. Otherwise, a shallow copy is made, so each variable in the new array's dataset is also a variable in this array's dataset. Use `data` to create a new object with the same structure as original but entirely new data. Parameters deep : bool, optional Whether the data array and its coordinates are loaded into memory and copied onto the new object. Default is True. data : array_like, optional Data to use in the new object. Must have same shape as original. When `data` is used, `deep` is ignored for all data variables, and only used for coords. Returns ------- object : DataArray New object with dimensions, attributes, coordinates, name, encoding, and optionally data copied from original. Examples -------- Shallow versus deep copy <xarray.DataArray (x: 3)> array([1, 2, 3]) Coordinates: * x (x) <U1 'a' 'b' 'c' <xarray.DataArray (x: 3)> array([7, 2, 3]) Coordinates: * x (x) <U1 'a' 'b' 'c' <xarray.DataArray (x: 3)> array([7, 2, 3]) Coordinates: * x (x) <U1 'a' 'b' 'c' Changing the data using the ``data`` argument maintains the structure of the original object, but with the new data. Original object is unaffected. <xarray.DataArray (x: 3)> array([ 0.1, 0.2, 0.3]) Coordinates: * x (x) <U1 'a' 'b' 'c' <xarray.DataArray (x: 3)> array([1, 2, 3]) Coordinates: * x (x) <U1 'a' 'b' 'c' See also -------- pandas.DataFrame.copy """

        variable = self.variable.copy(deep=deep, data=data)
        coords = OrderedDict((k, v.copy(deep=deep))
                             for k, v in self._coords.items())
        return self._replace(variable, coords)