


How Do I Properly Call Parent Class `__init__` Methods in Multiple Inheritance?
Calling Parent Class init with Multiple Inheritance
In scenarios where multiple inheritance is used, it's crucial to ensure that all parent class constructors are called. Two common approaches are:
- ParentClass.__init__(self) (old-style)
- super(DerivedClass, self).__init__() (newer-style)
However, if parent classes do not follow a consistent convention, these approaches may fail.
Determining the Correct Approach
The appropriate approach depends on whether the base classes are designed for multiple inheritance:
1. Standalone Base Classes
- Not designed for multiple inheritance.
-
Manually call each parent constructor using either:
- Without super: Foo.__init__(self), Bar.__init__(self)
- With super: super().__init__() (for all constructors up to Foo), super(Foo, self).__init__(bar) (for all constructors after Foo)
2. Mixins
- Designed for multiple inheritance.
- Use super().__init__() in the mixin class, which automatically calls the next constructor.
- Inherit from the mixin first, e.g., class FooBar(FooMixin, Bar).
3. Classes Designed for Cooperative Inheritance
- Similar to mixins, but all arguments are passed as keyword arguments.
- Call super().__init__() in all classes.
- Order of base classes does not matter.
Additional Considerations
- For object subclasses, avoid calling super().__init__().
- For standalone classes, always provide an empty constructor if directly inheriting from object (e.g., class Base(object): def __init__(self): pass).
Ultimately, the correct implementation depends on the classes involved. If a class is designed for multiple inheritance, it should be documented accordingly. Otherwise, assume it's not designed for such scenarios.
The above is the detailed content of How Do I Properly Call Parent Class `__init__` Methods in Multiple Inheritance?. For more information, please follow other related articles on the PHP Chinese website!

Hot AI Tools

Undresser.AI Undress
AI-powered app for creating realistic nude photos

AI Clothes Remover
Online AI tool for removing clothes from photos.

Undress AI Tool
Undress images for free

Clothoff.io
AI clothes remover

AI Hentai Generator
Generate AI Hentai for free.

Hot Article

Hot Tools

Notepad++7.3.1
Easy-to-use and free code editor

SublimeText3 Chinese version
Chinese version, very easy to use

Zend Studio 13.0.1
Powerful PHP integrated development environment

Dreamweaver CS6
Visual web development tools

SublimeText3 Mac version
God-level code editing software (SublimeText3)

Hot Topics



Solution to permission issues when viewing Python version in Linux terminal When you try to view Python version in Linux terminal, enter python...

This article explains how to use Beautiful Soup, a Python library, to parse HTML. It details common methods like find(), find_all(), select(), and get_text() for data extraction, handling of diverse HTML structures and errors, and alternatives (Sel

Serialization and deserialization of Python objects are key aspects of any non-trivial program. If you save something to a Python file, you do object serialization and deserialization if you read the configuration file, or if you respond to an HTTP request. In a sense, serialization and deserialization are the most boring things in the world. Who cares about all these formats and protocols? You want to persist or stream some Python objects and retrieve them in full at a later time. This is a great way to see the world on a conceptual level. However, on a practical level, the serialization scheme, format or protocol you choose may determine the speed, security, freedom of maintenance status, and other aspects of the program

Python's statistics module provides powerful data statistical analysis capabilities to help us quickly understand the overall characteristics of data, such as biostatistics and business analysis. Instead of looking at data points one by one, just look at statistics such as mean or variance to discover trends and features in the original data that may be ignored, and compare large datasets more easily and effectively. This tutorial will explain how to calculate the mean and measure the degree of dispersion of the dataset. Unless otherwise stated, all functions in this module support the calculation of the mean() function instead of simply summing the average. Floating point numbers can also be used. import random import statistics from fracti

This article compares TensorFlow and PyTorch for deep learning. It details the steps involved: data preparation, model building, training, evaluation, and deployment. Key differences between the frameworks, particularly regarding computational grap

This tutorial builds upon the previous introduction to Beautiful Soup, focusing on DOM manipulation beyond simple tree navigation. We'll explore efficient search methods and techniques for modifying HTML structure. One common DOM search method is ex

This article guides Python developers on building command-line interfaces (CLIs). It details using libraries like typer, click, and argparse, emphasizing input/output handling, and promoting user-friendly design patterns for improved CLI usability.

The article discusses popular Python libraries like NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, Django, Flask, and Requests, detailing their uses in scientific computing, data analysis, visualization, machine learning, web development, and H
