Fix HDF5 dataset consistency

[AntonioMirarchi]

This PR ensures consistency when using the HDF5 dataset class. Specifically, when self.cached=True, the _preload_data(self) function correctly handles cases where the embed 1D array is shared across all samples. When self.cached=False, the dataset instead processes tensors directly, assuming:

tensor_input = [[d[i]]] if d.ndim == 1 else d[i]

However, this assumption caused errors when embed was shared across samples, as it incorrectly indexed node i within the 1D embed array.

This PR fixes the issue and includes a pytest to verify consistency with and without caching.

[AntonioMirarchi]

@stefdoerr @sef43 can you review this?

Only one consideration regarding the need to obtain a tensor with torch.Size([1, 1]) if d.ndim == 1: previously, [[d[i]]] was used, whereas now, to maintain consistency with the _preload_data() function, I return a tensor of torch.Size([1]) using [d[i]]. I think it's fine because module.py takes care of it here.

[stefdoerr]

Can't we change the dataloader to tile the atom types instead of duplicating information in the file (and wasting space on disk?)

[AntonioMirarchi]

If you are referring to the write_as_hdf5() function that is used only in tests/test_datasets.py