A minimal code snippet

import torch
import torchvision
import detoxai


model = torchvision.models.resnet18(pretrained=True)
model.fc = torch.nn.Linear(model.fc.in_features, 2)  # Make it binary classification

X = torch.rand(128, 3, 224, 224)
Y = torch.randint(0, 2, size=(128,))
PA = torch.randint(0, 2, size=(128,))

dataloader = torch.utils.data.DataLoader(list(zip(X, Y, PA)), batch_size=32)

results: dict[str, detoxai.CorrectionResult] = detoxai.debias(model, dataloader)