[ao][sparsity] Support for sparsifying data operations on raw torch tensors.
The users can now pass in raw torch tensors and the base class handles all the parametrizations and masking
Example -
>>> data_list = [('tensor_1', torch.randn(3,3)), ('tensor_2', torch.randn(4,4))]
>>> defaults = {'sparsity_level': 0.7}
>>> sparsifier = DerivedDataSparsifier(data_list = data_list, **defaults) # Some sparsifier that inherits BaseDataSparsifier
>>> new_tensor_to_add = {'name': 'tensor_3', 'data': torch.randn(5,5), 'sparsity_level': 0.3}
>>> sparsifier.add_data(**new_tensor_to_add)
>>> # tensor_1 and tensor_2 will have sparsity_level of 0.7 but tensor_3 will have sparsity_level=0.3
Test Plan:
```python test/test_ao_sparsity.py TestBaseDataSparsifier```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/79252
Approved by: https://github.com/HDCharles, https://github.com/z-a-f
