def __init__(self, x: Union[(List[float], np.ndarray)], fval: float, variables: List[Variable], replacements: Dict[(str, Tuple[(str, int)])], history: Tuple[(List[MinimumEigenOptimizationResult], OptimizationResult)]) -> None:
    '\n        Constructs an instance of the result class.\n\n        Args:\n            x: the optimal value found in the optimization.\n            fval: the optimal function value.\n            variables: the list of variables of the optimization problem.\n            replacements: a dictionary of substituted variables. Key is a variable being\n                substituted, value is a tuple of substituting variable and a weight, either 1 or -1.\n            history: a tuple containing intermediate results. The first element is a list of\n                :class:`~qiskit.optimization.algorithms.MinimumEigenOptimizerResult` obtained by\n                invoking :class:`~qiskit.optimization.algorithms.MinimumEigenOptimizer` iteratively,\n                the second element is an instance of\n                :class:`~qiskit.optimization.algorithm.OptimizationResult` obtained at the last step\n                via `min_num_vars_optimizer`.\n        '
    super().__init__(x, fval, variables, None)
    self._replacements = replacements
    self._history = history
Constructs an instance of the result class.

Args:
    x: the optimal value found in the optimization.
    fval: the optimal function value.
    variables: the list of variables of the optimization problem.
    replacements: a dictionary of substituted variables. Key is a variable being
        substituted, value is a tuple of substituting variable and a weight, either 1 or -1.
    history: a tuple containing intermediate results. The first element is a list of
        :class:`~qiskit.optimization.algorithms.MinimumEigenOptimizerResult` obtained by
        invoking :class:`~qiskit.optimization.algorithms.MinimumEigenOptimizer` iteratively,
        the second element is an instance of
        :class:`~qiskit.optimization.algorithm.OptimizationResult` obtained at the last step
        via `min_num_vars_optimizer`.
def __init__(self, x: Union[(List[float], np.ndarray)], fval: float, variables: List[Variable], replacements: Dict[(str, Tuple[(str, int)])], history: Tuple[(List[MinimumEigenOptimizationResult], OptimizationResult)]) -> None:
    '\n        Constructs an instance of the result class.\n\n        Args:\n            x: the optimal value found in the optimization.\n            fval: the optimal function value.\n            variables: the list of variables of the optimization problem.\n            replacements: a dictionary of substituted variables. Key is a variable being\n                substituted, value is a tuple of substituting variable and a weight, either 1 or -1.\n            history: a tuple containing intermediate results. The first element is a list of\n                :class:`~qiskit.optimization.algorithms.MinimumEigenOptimizerResult` obtained by\n                invoking :class:`~qiskit.optimization.algorithms.MinimumEigenOptimizer` iteratively,\n                the second element is an instance of\n                :class:`~qiskit.optimization.algorithm.OptimizationResult` obtained at the last step\n                via `min_num_vars_optimizer`.\n        '
    super().__init__(x, fval, variables, None)
    self._replacements = replacements
    self._history = history