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snapshot for August 2025
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29 August 2025
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Does anyone know about tensorflow and tensorflow-lite. Need a urgent hep
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Guys I have a question, is it possible to pass an existing object to the child's init arguments without keep rewriting it? let's say I have a grandparent, parent and child class. I have made an object of grandparent, now when I want to create an object of child class, I literally have to fill grandparent's parameters over again.
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I m trying to convert ssd-mobilenet-v2 tensorflow model into .tflite fully quantised int8 model. And after the conversion, the model is detecting objects though there is no object in the frame
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I will share the code details that I have used
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class Fruit: def __init__(self, name, color): self.name = name self.color = color class Apple(Fruit): def __init__(self, name, color, variety, region): super().__init__(name, color) self.variety = variety self.region = region def describe(): print(f"{self.name, self.color, self.variety, self.region} are correct.") x = Fruit("Apple", "red") y = Apple("Apple", "red", "Fuji", "X_place") z = Apple(x, "Fuji", "X_place") print(x) print(y) print(z) now if you print, I'll get error that I'm missing one argument in z. I don't want to keep refilling parent's parameters everytime I create a new object.
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Artanclass Fruit: def __init__(self, name, color): self.name = name self.color = color class Apple(Fruit): def __init__(self, name, color, variety, region): super().__init__(name, color) self.variety = variety self.region = region def describe():
If parameters exist they must be defined, but as a workaround you can add default values ​​for parameters. Some like this def my_function(country = "Norway"): print("I am from " + country) my_function("Sweden") my_function("India") my_function()
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