def make_atom14_masks(protein: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
    """Construct denser atom positions (14 dimensions instead of 37)."""
    restype_atom14_to_atom37_list = []
    restype_atom37_to_atom14_list = []
    restype_atom14_mask_list = []

    for rt in rc.restypes:
        atom_names = rc.restype_name_to_atom14_names[rc.restype_1to3[rt]]
        restype_atom14_to_atom37_list.append([(rc.atom_order[name] if name else 0) for name in atom_names])
        atom_name_to_idx14 = {name: i for i, name in enumerate(atom_names)}
        restype_atom37_to_atom14_list.append(
            [(atom_name_to_idx14[name] if name in atom_name_to_idx14 else 0) for name in rc.atom_types]
        )

        restype_atom14_mask_list.append([(1.0 if name else 0.0) for name in atom_names])

    # Add dummy mapping for restype 'UNK'
    restype_atom14_to_atom37_list.append([0] * 14)
    restype_atom37_to_atom14_list.append([0] * 37)
    restype_atom14_mask_list.append([0.0] * 14)

    restype_atom14_to_atom37 = torch.tensor(
        restype_atom14_to_atom37_list,
        dtype=torch.int32,
        device=protein["aatype"].device,
    )
    restype_atom37_to_atom14 = torch.tensor(
        restype_atom37_to_atom14_list,
        dtype=torch.int32,
        device=protein["aatype"].device,
    )
    restype_atom14_mask = torch.tensor(
        restype_atom14_mask_list,
        dtype=torch.float32,
        device=protein["aatype"].device,
    )
    protein_aatype = protein["aatype"].to(torch.long)

    # create the mapping for (residx, atom14) --> atom37, i.e. an array
    # with shape (num_res, 14) containing the atom37 indices for this protein
    residx_atom14_to_atom37 = restype_atom14_to_atom37[protein_aatype]
    residx_atom14_mask = restype_atom14_mask[protein_aatype]

    protein["atom14_atom_exists"] = residx_atom14_mask
    protein["residx_atom14_to_atom37"] = residx_atom14_to_atom37.long()

    # create the gather indices for mapping back
    residx_atom37_to_atom14 = restype_atom37_to_atom14[protein_aatype]
    protein["residx_atom37_to_atom14"] = residx_atom37_to_atom14.long()

    # create the corresponding mask
    restype_atom37_mask = torch.zeros([21, 37], dtype=torch.float32, device=protein["aatype"].device)
    for restype, restype_letter in enumerate(rc.restypes):
        restype_name = rc.restype_1to3[restype_letter]
        atom_names = rc.residue_atoms[restype_name]
        for atom_name in atom_names:
            atom_type = rc.atom_order[atom_name]
            restype_atom37_mask[restype, atom_type] = 1

    residx_atom37_mask = restype_atom37_mask[protein_aatype]
    protein["atom37_atom_exists"] = residx_atom37_mask

    return protein
Construct denser atom positions (14 dimensions instead of 37).
dpkd/transformers/src/transformers/models/esm/openfold_utils/data_transforms.py