BUG: Pytorch DeepExplainer SHAP explanations do not sum up to the model's output about shap HOT 3 OPEN

import shap
import torch
from torch import nn
from torch import optim
from torch.nn import functional as F
from torch.nn.functional import binary_cross_entropy
from sklearn.datasets import make_classification

device = (
    "cuda"
    if torch.cuda.is_available()
    else "mps" if torch.backends.mps.is_available() else "cpu"
)

N_EPOCHS = 10
LEARNING_RATE = 0.1

class LogisticRegression(nn.Module):
    def __init__(self, num_features):
        super().__init__()
        self.linear = nn.Linear(num_features, 1)

    def forward(self, vec):
        return F.sigmoid(self.linear(vec))

    def save_model(self, loc):
        with open(loc, "w", encoding="utf-8") as file:
            model_params = ""
            for n, param in enumerate(self.parameters()):
                model_params += f"{n},{param.data}\n"
            file.write(model_params)


model = LogisticRegression(num_features=6).to(device)

optimizer = optim.SGD(model.parameters(), lr=LEARNING_RATE)

X,y = make_classification(n_samples=1000,n_features=6,n_informative=4)
X = torch.as_tensor(X, dtype=torch.float32).to(device)
y = torch.as_tensor(y, dtype=torch.float32).unsqueeze(dim=1).to(device)

for i in range(N_EPOCHS):
    model.zero_grad()
    y_pred = model(X)
    loss = binary_cross_entropy(y_pred, y)
    loss.backward()
    optimizer.step()

e = shap.DeepExplainer(model, X)

shap_values = e.shap_values(X, check_additivity=True)```