You return four variables s1,s2,s3,s4 and receive them using a single variable obj. This is what is called a tuple, obj is associated with 4 values, the values of s1,s2,s3,s4. So, use index as you use in a list to get the value you want, in order.

Copyobj=list_benefits()
print obj[0] + " is a benefit of functions!"
print obj[1] + " is a benefit of functions!"
print obj[2] + " is a benefit of functions!"
print obj[3] + " is a benefit of functions!"

It appears that the values of split_dict are tuples and not what I assume to be expected np.array's. I would recommend taking a look at what function split is returning to split_dict because it might be creating tuples instead of np.array's.

Edit:

Based on what's inside function split, it's returning {0: (array([0, 2], dtype=int64),), 1: (array([1, 3, 4, 5], dtype=int64),)} to split_dict so the values are tuples that contain a numpy.array and the data type (in this case int64) as elements, thus raising the AttributeError.

A slightly modified split that does what you're looking for would look something like this:

def split(array):
    N={}
    uniqe_array=np.unique(array)
    for i in uniqe_array:
        N[i]=np.where(array==i)[0]     #Notice change here to take first element 
    return N

See this answer for more information: What is the purpose of numpy.where returning a tuple?

According to the error you posted, Data is of type tuple and there is no attribute shape defined for data. You could try casting Data when you call your preprocess function, e.g.:

preprocess(numpy.array(Data))

In fact, this means you have multiple versions of numpy installed somehow (or there are multiple versions that are overlapping). You need to make sure that numpy is fully uninstalled from your system, then reinstall.

For me, I did

pip uninstall numpy
sudo apt-get purge python3-numpy

Then I had to go to /usr/local/lib/python3.6/dist-packages and delete the numpy folders that were still there for some reason. After that, reinstalling numpy with

pip install numpy

worked. Here is the github issue I opened on it:

https://github.com/numpy/numpy/issues/12775

I assume your inputs should be NumPy arrays. You are passing tuples (indicated by the parentheses ()). It might be enough to do this:

import numpy as np

X = np.array(([0, 0, 0, 1], [0, 0, 1, 0], [0, 1, 0, 0], [1, 0, 0, 0]))    
y = np.array(([0, 0], [0, 1], [1, 0], [1, 1]))

It literally means that the the tuple class in Python doesn't have a method called to. Since you're trying to put your labels onto your device, just do labels = torch.tensor(labels).to(device).

If you don't want to do this, you can change the way the DataLoader works by making it return your labels as a PyTorch tensor rather than a tuple.

Edit

Since the labels seem to be strings, I would convert them to one-hot encoded vectors first:

>>> import torch
>>> labels_unique = set(labels)
>>> keys = {key: value for key, value in zip(labels_unique, range(len(labels_unique)))}
>>> labels_onehot = torch.zeros(size=(len(labels), len(keys)))
>>> for idx, label in enumerate(labels_onehot):
...     labels_onehot[idx][keys[label]] = 1
...
>>> labels_onehot = labels.to(device)

I'm shooting a bit in the dark here because I don't know the details exactly, but yeah strings won't work with tensors.