Segformer output not determinsitic

Hi! Thanks for the library. I'm using a Segformer model, and the same input through the same model is giving me always two different outputs. I leave one example here. ```python # Imports import segmentation_models_pytorch as smp from PIL.Image import open as open_image from PIL.Image import FLIP_LEFT_RIGHT import numpy as np import matplotlib.pyplot as plt import pandas as pd import random, torch from pathlib import Path import torch random.seed(0) np.random.seed(0) torch.manual_seed(0) torch.cuda.manual_seed_all(0) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False torch.use_deterministic_algorithms(True) # ... Some other code device = "cpu" model = smp.Segformer(**model_params) model.load_state_dict("my/checkpoint.ckpt").to(device) image = open_image("some/path.png") images = [image, image] # The same image with torch.inference_mode(): images_tensor = torch.utils.data.default_collate([inference_transform(img) for img in images]) assert torch.allclose(images_tensor[0], images_tensor[1]) # 1st assert mask_predictions = torch.sigmoid(self(images_tensor.to(model.device))) assert torch.allclose(mask_predictions[0], mask_predictions[1]) # 2nd assert ```` Assert # 1 does not raise any errors (the two elements being input to the network are really the same) However, assert # 2 raises an error, as the outputs are not the same. Any idea of what could be happening? Thanks.