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import torch
from torch import nn
import torch.nn.functional as F

class Ridge:
    def __init__(self, alpha = 0, fit_intercept = True,):
        self.alpha = alpha
        self.fit_intercept = fit_intercept
        
    def fit(self, X: torch.tensor, y: torch.tensor) -> None:

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import argparse
from loguru import logger

parser = argparse.ArgumentParser(description='Minimal example of dumping args into log file.')
parser.add_argument('--flag',type=str,
                    help='Some parameter flag')
args = parser.parse_args()

logger.add("file_{time}.log")
for arg, value in sorted(vars(args).items()):

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p(c = True | tar = True, smoking = True) = 0.99
p(c = True | tar = False, smoking = True) = 0.2
p(c = True | tar = False, smoking = False) = 0.01
p(c = True | tar = True, smoking = False) = 0.99

p(smoking = True) = 0.1
p(smoking = False) = 0.9
p(tar = True | smoking = True) = 0.99
p(tar = False | smoking = True) = 0.01

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from datetime import datetime
now = datetime.utcnow().strftime("%Y%m%d%H%M%S")
root_logdir = "tf_logs"
logdir = "{}/run-{}/".format(root_logdir, now)