# Buggy code
def eval(self, expr, inplace=False, **kwargs):
    """Evaluate a Python expression as a string using various backends.
    Args:
        expr: The expression to evaluate. This string cannot contain any
            Python statements, only Python expressions.

        parser: The parser to use to construct the syntax tree from the
            expression. The default of 'pandas' parses code slightly
            different than standard Python. Alternatively, you can parse
            an expression using the 'python' parser to retain strict
            Python semantics. See the enhancing performance documentation
            for more details.

        engine: The engine used to evaluate the expression.

        truediv: Whether to use true division, like in Python >= 3

        local_dict: A dictionary of local variables, taken from locals()
            by default.

        global_dict: A dictionary of global variables, taken from
            globals() by default.

        resolvers: A list of objects implementing the __getitem__ special
            method that you can use to inject an additional collection
            of namespaces to use for variable lookup. For example, this is
            used in the query() method to inject the index and columns
            variables that refer to their respective DataFrame instance
            attributes.

        level: The number of prior stack frames to traverse and add to
            the current scope. Most users will not need to change this
            parameter.

        target: This is the target object for assignment. It is used when
            there is variable assignment in the expression. If so, then
            target must support item assignment with string keys, and if a
            copy is being returned, it must also support .copy().

        inplace: If target is provided, and the expression mutates target,
            whether to modify target inplace. Otherwise, return a copy of
            target with the mutation.
    Returns:
        ndarray, numeric scalar, DataFrame, Series
    """
    self._validate_eval_query(expr, **kwargs)
    inplace = validate_bool_kwarg(inplace, "inplace")
    new_query_compiler = self._query_compiler.eval(expr, **kwargs)
    return_type = type(
        pandas.DataFrame(columns=self.columns).astype(self.dtypes).eval(expr, **kwargs)
    ).__name__
    if return_type == self.__name__:
        return self._create_or_update_from_compiler(new_query_compiler, inplace)
    else:
        if inplace:
            raise ValueError("Cannot operate inplace if there is no assignment")
        return getattr(sys.modules[self.__module__], return_type)(
            query_compiler=new_query_compiler
        )


# Traceback (RecursionError)
Traceback (most recent call last):
File "1.py", line 14, in <module>
ref = df["a"].add(df["b"])
File "/localdisk/ilyaenko/modin/modin/pandas/series.py", line 376, in add
return super(Series, new_self).add(
File "/localdisk/ilyaenko/modin/modin/pandas/series.py", line 376, in add
return super(Series, new_self).add(
File "/localdisk/ilyaenko/modin/modin/pandas/series.py", line 376, in add
return super(Series, new_self).add(
[Previous line repeated 978 more times]
...
RecursionError: maximum recursion depth exceeded while calling a Python object

# Fixed code
def eval(self, expr, inplace=False, **kwargs):
    """Evaluate a Python expression as a string using various backends.
    Args:
        expr: The expression to evaluate. This string cannot contain any
            Python statements, only Python expressions.

        parser: The parser to use to construct the syntax tree from the
            expression. The default of 'pandas' parses code slightly
            different than standard Python. Alternatively, you can parse
            an expression using the 'python' parser to retain strict
            Python semantics. See the enhancing performance documentation
            for more details.

        engine: The engine used to evaluate the expression.

        truediv: Whether to use true division, like in Python >= 3

        local_dict: A dictionary of local variables, taken from locals()
            by default.

        global_dict: A dictionary of global variables, taken from
            globals() by default.

        resolvers: A list of objects implementing the __getitem__ special
            method that you can use to inject an additional collection
            of namespaces to use for variable lookup. For example, this is
            used in the query() method to inject the index and columns
            variables that refer to their respective DataFrame instance
            attributes.

        level: The number of prior stack frames to traverse and add to
            the current scope. Most users will not need to change this
            parameter.

        target: This is the target object for assignment. It is used when
            there is variable assignment in the expression. If so, then
            target must support item assignment with string keys, and if a
            copy is being returned, it must also support .copy().

        inplace: If target is provided, and the expression mutates target,
            whether to modify target inplace. Otherwise, return a copy of
            target with the mutation.
    Returns:
        ndarray, numeric scalar, DataFrame, Series
    """
    self._validate_eval_query(expr, **kwargs)
    inplace = validate_bool_kwarg(inplace, "inplace")
    new_query_compiler = self._query_compiler.eval(expr, **kwargs)
    return_type = type(
        pandas.DataFrame(columns=self.columns).astype(self.dtypes).eval(expr, **kwargs)
    ).__name__
    if return_type == type(self).__name__:
        return self._create_or_update_from_compiler(new_query_compiler, inplace)
    else:
        if inplace:
            raise ValueError("Cannot operate inplace if there is no assignment")
        return getattr(sys.modules[self.__module__], return_type)(
            query_compiler=new_query_compiler
        )