fixed typing error of anchor_gt_residue

openfold/utils/multi_chain_permutation.py

@@ -314,7 +314,7 @@ def split_ground_truth_labels(gt_features: dict) -> List[Dict]:
     return labels
 
 
-def get_per_asym_residue_index(features: dict) -> Dict[int, list]:
+def get_per_asym_residue_index(features: dict) -> Dict[int, torch.Tensor]:
     """
     A function that retrieve which residues belong to which asym_id 
 
@@ -354,7 +354,7 @@ def get_entity_2_asym_list(features: dict) -> Dict[int, list]:
 
 
 def calculate_input_mask(true_ca_masks: List[torch.Tensor], anchor_gt_idx: torch.Tensor, 
-                         anchor_gt_residue: list,
+                         anchor_gt_residue: torch.Tensor,
                          asym_mask: torch.Tensor, pred_ca_mask: torch.Tensor) -> torch.Tensor:
     """
     Calculate an input mask for downstream optimal transformation computation
@@ -362,7 +362,7 @@ def calculate_input_mask(true_ca_masks: List[torch.Tensor], anchor_gt_idx: torch
     Args:
         true_ca_masks: list of masks from ground truth chains.
         anchor_gt_idx (Tensor): a tensor with one integer in it. The index of selected ground truth anchor.
-        anchor_gt_residue:a list of residue indexes that belongs to the selected ground truth anchor
+        anchor_gt_residue:a 1D vector tensor of residue indexes that belongs to the selected ground truth anchor
         asym_mask (Tensor): Boolean tensor indicating which regions are selected predicted anchor.
         pred_ca_mask (Tensor): ca mask from predicted structure.
 
@@ -378,7 +378,7 @@ def calculate_input_mask(true_ca_masks: List[torch.Tensor], anchor_gt_idx: torch
 
 
 def calculate_optimal_transform(true_ca_poses: List[torch.Tensor],
-                                anchor_gt_idx: int, anchor_gt_residue: list,
+                                anchor_gt_idx: int, anchor_gt_residue: torch.Tensor,
                                 true_ca_masks: List[torch.Tensor], pred_ca_mask: torch.Tensor,
                                 asym_mask: torch.Tensor,
                                 pred_ca_pos: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
@@ -391,7 +391,7 @@ def calculate_optimal_transform(true_ca_poses: List[torch.Tensor],
     Args: 
         true_ca_poses: a list of tensors, corresponding to the c-alpha positions of the ground truth structure. e.g. If there are 5 chains, this list will have a length of 5
         anchor_gt_idx (Tensor): a tensor with one integer in it. The index of selected ground truth anchor.
-        anchor_gt_residue:a list of residue indexes that belongs to the selected ground truth anchor
+        anchor_gt_residue:a 1D vector tensor of residue indexes that belongs to the selected ground truth anchor
         true_ca_masks: list of masks from ground truth chains e.g. it will be length=5 if there are 5 chains in ground truth structure
         pred_ca_mask: A boolean tensor corresponds to the mask to mask the predicted features
         asym_mask: A boolean tensor that mask out other elements in a tensor if they do not belong to a this asym_id