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class A:
    out1 = 2
    out2 = 3
    out3 = 4
a = {}
for i in range(1,4):
    a[f'out{i}'] = getattr(A,f'out{i}')
print(a)

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https://blog.nelsonliu.me/2020/10/13/newer-pytorch-binaries-for-older-gpus/

https://github.com/nelson-liu/pytorch-manylinux-binaries/releases

https://cs.stanford.edu/~nfliu/files/pytorch/whl/torch_stable.html

pip3 install torch==1.9.1+cu111 -f https://nelsonliu.me/files/pytorch/whl/torch_stable.html
pip3 install torchvision==0.10.1+cu111 torchaudio==0.9.1 -f https://download.pytorch.org/whl/torch_stable.html

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import numpy as np
x = [6,7,8]
y = np.expand_dims(x, axis=0)

my = [1,2,3]
mx = [4,5,6]
yx = np.tensordot(my,mx,0)

res = np.where(yx == 1)

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if not os.path.exists("output"):
  os.makedirs("output")

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class Address:
    def __init__(self, street, city, state, zipcode, street2=''):
        self.street = street
        self.street2 = street2
        self.city = city
        self.state = state
        self.zipcode = zipcode

    def __str__(self):
        lines = [self.street]

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# use of class method and static method.
from datetime import date
  
class Person:
    def __init__(self, name, age):
        self.name = name
        self.age = age
      
    # a class method to create a Person object by birth year.
    @classmethod

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dataset, val_set = torch.utils.data.random_split(dataset,[int(len(dataset) * 0.85), 
                                                 len(dataset) - int(len(dataset) * 0.85)])

config.manual_seed = 33
print("Random Seed: ", config.manual_seed)
random.seed(config.manual_seed)
torch.manual_seed(config.manual_seed)

if torch.cuda.is_available():
    torch.cuda.manual_seed_all(config.manual_seed)

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How to install OpenCV 4.5.2 with CUDA 11.2 and CUDNN 8.2 in Ubuntu 20.04

First of all install update and upgrade your system:

    $ sudo apt update
    $ sudo apt upgrade

Then, install required libraries:

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#!/bin/bash

import subprocess
subprocess.run(["./zip.sh"])
subprocess.run(["zip", "-r", "test.zip", "test/"], capture_output=True)

# https://docs.python.org/3/library/subprocess.html

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#!/bin/bash

## https://deeplearningcrashcourse.org/setup_ubuntu/

### steps ####
# verify the system has a cuda-capable gpu
# download and install the nvidia cuda toolkit and cudnn
# setup environmental variables
# verify the installation
###