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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 _create_method(cls, op, coerce_to_dtype=True):
 """ A class method that returns a method that will correspond to an operator for an ExtensionArray subclass, by dispatching to the relevant operator defined on the individual elements of the ExtensionArray. Parameters op : function An operator that takes arguments op(a, b) coerce_to_dtype : bool, default True boolean indicating whether to attempt to convert the result to the underlying ExtensionArray dtype. If it's not possible to create a new ExtensionArray with the values, an ndarray is returned instead. Returns ------- Callable[[Any, Any], Union[ndarray, ExtensionArray]] A method that can be bound to a class. When used, the method receives the two arguments, one of which is the instance of this class, and should return an ExtensionArray or an ndarray. Returning an ndarray may be necessary when the result of the `op` cannot be stored in the ExtensionArray. The dtype of the ndarray uses NumPy's normal inference rules. Example ------- Given an ExtensionArray subclass called MyExtensionArray, use in the class definition of MyExtensionArray to create the operator for addition, that will be based on the operator implementation of the underlying elements of the ExtensionArray """


        def _binop(self, other):
            def convert_values(param):
                if isinstance(param, ExtensionArray) or is_list_like(param):
                    ovalues = param
                else:  # Assume its an object
                    ovalues = [param] * len(self)
                return ovalues

            if isinstance(other, (ABCSeries, ABCIndexClass)):
                # rely on pandas to unbox and dispatch to us
                return NotImplemented

            lvalues = self
            rvalues = convert_values(other)

            # If the operator is not defined for the underlying objects,
            # a TypeError should be raised
            res = [op(a, b) for (a, b) in zip(lvalues, rvalues)]

            def _maybe_convert(arr):
                if coerce_to_dtype:
                    # https://github.com/pandas-dev/pandas/issues/22850
                    # We catch all regular exceptions here, and fall back
                    # to an ndarray.
                    try:
                        res = self._from_sequence(arr)
                    except Exception:
                        res = np.asarray(arr)
                else:
                    res = np.asarray(arr)
                return res

            if op.__name__ in {'divmod', 'rdivmod'}:
                a, b = zip(*res)
                res = _maybe_convert(a), _maybe_convert(b)
            else:
                res = _maybe_convert(res)
            return res

        op_name = ops._get_op_name(op, True)
        return set_function_name(_binop, op_name, cls)