Using PyTorch tensors with scikit-learn
Can I use PyTorch tensors instead of NumPy arrays while working with scikit-learn?
I tried some methods from scikit-learn like train_test_split and StandardScalar, and it seems to work just fine, but is there anything I should know when I'm using PyTorch tensors instead of NumPy arrays?
According to this question on https://scikit-learn.org/stable/faq.html#how-can-i-load-my-own-datasets-into-a-format-usable-by-scikit-learn :

numpy arrays or scipy sparse matrices. Other types that are convertible to numeric arrays such as pandas DataFrame are also acceptable.

Does that mean using PyTorch tensors is completely safe?

I don't think PyTorch tensors are directly supported by scikit-learn. But you can always get the underlying numpy array from PyTorch tensors
my_nparray = my_tensor.numpy()

and then use it with scikit learn functions.
