方法__add__被称为dunder（“双下划线”）或魔术方法。__add__用于为任何类的+操作符定义行为，而不仅仅是在Numpy中，并且您不需要做任何特殊的事情来使+触发__add__。
几乎所有的操作员都有类似的神奇方法。
来自Python文档（https：//docs.python.org/3/reference/datamodel.html#object.%5F%5Fadd%5F%5F）：
要计算表达式x + y，其中x是具有__add__（）方法的类的示例，类型（x）.add（x，y）被调用。

Python运算符重载和Python数据模型是可以让你更清楚一点的主题。
更简洁地说，当你调用one_thing + another_thing时，python解释器会调用one_thing.__add__(another_thing)，但是如果one_thing没有实现__add__，解释器会尝试another_thing.__radd__(one_thing)。否则你会得到一个Exception。
当然，你可以实现你自己版本的__add__方法，当你这样做的时候，你的对象就能够处理“+”加运算符了，这就是你的问题的例子中发生的事情。
下面你可以看到这样的例子：

• https://realpython.com/operator-function-overloading/
• https://docs.python.org/3/reference/datamodel.html#object.%5C_%5C_add%5C_%5C_
• https://www.geeksforgeeks.org/operator-overloading-in-python/的

看看NDArrayOperatorsMixin，特别是这里。它说它使用array_ufunc。这是它如何将运算符Map到方法。此外，请看这里和这里以及这里，看看它实际上是如何处理的。如果你想有更深入的了解，那么你需要自己调试。
以下是截至2023年11月18日的mixins.py的原始代码：

"""Mixin classes for custom array types that don't inherit from ndarray."""
from numpy._core import umath as um

__all__ = ['NDArrayOperatorsMixin']

def _disables_array_ufunc(obj):
    """True when __array_ufunc__ is set to None."""
    try:
        return obj.__array_ufunc__ is None
    except AttributeError:
        return False

def _binary_method(ufunc, name):
    """Implement a forward binary method with a ufunc, e.g., __add__."""
    def func(self, other):
        if _disables_array_ufunc(other):
            return NotImplemented
        return ufunc(self, other)
    func.__name__ = '__{}__'.format(name)
    return func

def _reflected_binary_method(ufunc, name):
    """Implement a reflected binary method with a ufunc, e.g., __radd__."""
    def func(self, other):
        if _disables_array_ufunc(other):
            return NotImplemented
        return ufunc(other, self)
    func.__name__ = '__r{}__'.format(name)
    return func

def _inplace_binary_method(ufunc, name):
    """Implement an in-place binary method with a ufunc, e.g., __iadd__."""
    def func(self, other):
        return ufunc(self, other, out=(self,))
    func.__name__ = '__i{}__'.format(name)
    return func

def _numeric_methods(ufunc, name):
    """Implement forward, reflected and inplace binary methods with a ufunc."""
    return (_binary_method(ufunc, name),
            _reflected_binary_method(ufunc, name),
            _inplace_binary_method(ufunc, name))

def _unary_method(ufunc, name):
    """Implement a unary special method with a ufunc."""
    def func(self):
        return ufunc(self)
    func.__name__ = '__{}__'.format(name)
    return func

class NDArrayOperatorsMixin:
    """Mixin defining all operator special methods using __array_ufunc__.

    This class implements the special methods for almost all of Python's
    builtin operators defined in the `operator` module, including comparisons
    (``==``, ``>``, etc.) and arithmetic (``+``, ``*``, ``-``, etc.), by
    deferring to the ``__array_ufunc__`` method, which subclasses must
    implement.

    It is useful for writing classes that do not inherit from `numpy.ndarray`,
    but that should support arithmetic and numpy universal functions like
    arrays as described in `A Mechanism for Overriding Ufuncs
    <https://numpy.org/neps/nep-0013-ufunc-overrides.html>`_.

    As an trivial example, consider this implementation of an ``ArrayLike``
    class that simply wraps a NumPy array and ensures that the result of any
    arithmetic operation is also an ``ArrayLike`` object::

        class ArrayLike(np.lib.mixins.NDArrayOperatorsMixin):
            def __init__(self, value):
                self.value = np.asarray(value)

            # One might also consider adding the built-in list type to this
            # list, to support operations like np.add(array_like, list)
            _HANDLED_TYPES = (np.ndarray, numbers.Number)

            def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
                out = kwargs.get('out', ())
                for x in inputs + out:
                    # Only support operations with instances of _HANDLED_TYPES.
                    # Use ArrayLike instead of type(self) for isinstance to
                    # allow subclasses that don't override __array_ufunc__ to
                    # handle ArrayLike objects.
                    if not isinstance(x, self._HANDLED_TYPES + (ArrayLike,)):
                        return NotImplemented

                # Defer to the implementation of the ufunc on unwrapped values.
                inputs = tuple(x.value if isinstance(x, ArrayLike) else x
                               for x in inputs)
                if out:
                    kwargs['out'] = tuple(
                        x.value if isinstance(x, ArrayLike) else x
                        for x in out)
                result = getattr(ufunc, method)(*inputs, **kwargs)

                if type(result) is tuple:
                    # multiple return values
                    return tuple(type(self)(x) for x in result)
                elif method == 'at':
                    # no return value
                    return None
                else:
                    # one return value
                    return type(self)(result)

            def __repr__(self):
                return '%s(%r)' % (type(self).__name__, self.value)

    In interactions between ``ArrayLike`` objects and numbers or numpy arrays,
    the result is always another ``ArrayLike``:

        >>> x = ArrayLike([1, 2, 3])
        >>> x - 1
        ArrayLike(array([0, 1, 2]))
        >>> 1 - x
        ArrayLike(array([ 0, -1, -2]))
        >>> np.arange(3) - x
        ArrayLike(array([-1, -1, -1]))
        >>> x - np.arange(3)
        ArrayLike(array([1, 1, 1]))

    Note that unlike ``numpy.ndarray``, ``ArrayLike`` does not allow operations
    with arbitrary, unrecognized types. This ensures that interactions with
    ArrayLike preserve a well-defined casting hierarchy.

    .. versionadded:: 1.13
    """
    __slots__ = ()
    # Like np.ndarray, this mixin class implements "Option 1" from the ufunc
    # overrides NEP.

    # comparisons don't have reflected and in-place versions
    __lt__ = _binary_method(um.less, 'lt')
    __le__ = _binary_method(um.less_equal, 'le')
    __eq__ = _binary_method(um.equal, 'eq')
    __ne__ = _binary_method(um.not_equal, 'ne')
    __gt__ = _binary_method(um.greater, 'gt')
    __ge__ = _binary_method(um.greater_equal, 'ge')

    # numeric methods
    __add__, __radd__, __iadd__ = _numeric_methods(um.add, 'add')
    __sub__, __rsub__, __isub__ = _numeric_methods(um.subtract, 'sub')
    __mul__, __rmul__, __imul__ = _numeric_methods(um.multiply, 'mul')
    __matmul__, __rmatmul__, __imatmul__ = _numeric_methods(
        um.matmul, 'matmul')
    # Python 3 does not use __div__, __rdiv__, or __idiv__
    __truediv__, __rtruediv__, __itruediv__ = _numeric_methods(
        um.true_divide, 'truediv')
    __floordiv__, __rfloordiv__, __ifloordiv__ = _numeric_methods(
        um.floor_divide, 'floordiv')
    __mod__, __rmod__, __imod__ = _numeric_methods(um.remainder, 'mod')
    __divmod__ = _binary_method(um.divmod, 'divmod')
    __rdivmod__ = _reflected_binary_method(um.divmod, 'divmod')
    # __idivmod__ does not exist
    # TODO: handle the optional third argument for __pow__?
    __pow__, __rpow__, __ipow__ = _numeric_methods(um.power, 'pow')
    __lshift__, __rlshift__, __ilshift__ = _numeric_methods(
        um.left_shift, 'lshift')
    __rshift__, __rrshift__, __irshift__ = _numeric_methods(
        um.right_shift, 'rshift')
    __and__, __rand__, __iand__ = _numeric_methods(um.bitwise_and, 'and')
    __xor__, __rxor__, __ixor__ = _numeric_methods(um.bitwise_xor, 'xor')
    __or__, __ror__, __ior__ = _numeric_methods(um.bitwise_or, 'or')

    # unary methods
    __neg__ = _unary_method(um.negative, 'neg')
    __pos__ = _unary_method(um.positive, 'pos')
    __abs__ = _unary_method(um.absolute, 'abs')
    __invert__ = _unary_method(um.invert, 'invert')

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