Seems that the `ToTensor`

op gives problems. Most likely the best way to save data is in numpy format so that transforms do not complain.

Error:

```
TypeError: pic should be PIL Image or ndarray. Got <class 'torch.Tensor'>
```

sample code that gave error:

```
# saving torch tensors
import torch
import torch.nn as nn
import torchvision
from pathlib import Path
from collections import OrderedDict
path = Path('~/data/tmp/').expanduser()
path.mkdir(parents=True, exist_ok=True)
tensor_a = torch.rand(2,3)
tensor_b = torch.rand(1,3)
db = {'a': tensor_a, 'b': tensor_b}
torch.save(db, path/'torch_db')
loaded = torch.load(path/'torch_db')
print( loaded['a'] == tensor_a )
print( loaded['b'] == tensor_b )
# testing if ToTensor() screws things up
lb, ub = -1, 1
N, Din, Dout = 3, 1, 1
x = torch.distributions.Uniform(low=lb, high=ub).sample((N, Din))
print(x)
f = nn.Sequential(OrderedDict([
('f1', nn.Linear(Din,Dout)),
('out', nn.SELU())
]))
y = f(x)
transform = torchvision.transforms.transforms.ToTensor()
y_proc = transform(y)
print(y_proc)
```

reference for saving data in numpy:

```
# saving data in numpy
import numpy as np
import pickle
from pathlib import Path
path = Path('~/data/tmp/').expanduser()
path.mkdir(parents=True, exist_ok=True)
lb,ub = -1,1
num_samples = 5
x = np.random.uniform(low=lb,high=ub,size=(1,num_samples))
y = x**2 + x + 2
# using save (to npy), savez (to npz)
np.save(path/'x', x)
np.save(path/'y', y)
np.savez(path/'db', x=x, y=y)
with open(path/'db.pkl', 'wb') as db_file:
pickle.dump(obj={'x':x, 'y':y}, file=db_file)
## using loading npy, npz files
x_loaded = np.load(path/'x.npy')
y_load = np.load(path/'y.npy')
db = np.load(path/'db.npz')
with open(path/'db.pkl', 'rb') as db_file:
db_pkl = pickle.load(db_file)
print(x is x_loaded)
print(x == x_loaded)
print(x == db['x'])
print(x == db_pkl['x'])
print('done')
```

reference: https://stackoverflow.com/a/62883249/1601580