Automatic Conversion for uint16/uint32 to Compatible PyTorch Dtypes
This PR addresses an issue encountered when utilizing uint16 or uint32 datatypes with datasets, followed by attempting to convert these datasets into PyTorch-compatible formats. Currently, doing so results in a TypeError due to incompatible datatype conversion, as illustrated by the following example:
```python
from datasets import Dataset, Sequence, Value, Features

def gen():
    for i in range(100):
        yield {'seq': list(range(i, i + 20))}

ds = Dataset.from_generator(gen, features=Features({'seq': Sequence(feature=Value(dtype='uint16'), length=-1)}))

ds.set_format('torch')

print(ds[0])
```
This code snippet triggers the following error due to the inability to convert numpy.uint16 arrays to a PyTorch-supported format:
```
TypeError: can't convert np.ndarray of type numpy.uint16. The only supported types are: float64, float32, float16, complex64, complex128, int64, int32, int16, int8, uint8, and bool.
```

This PR introduces an automatic mechanism to convert np.uint16 and np.uint32 datatypes to np.int64 for seamless compatibility with PyTorch formats, simplifying workflows and improving developer experience by eliminating the need for manual conversion handling.