code: def build_extra_msa_feat(batch):
      msa_1hot = nn.functional.one_hot(batch["extra_msa"], 23)
      msa_feat = [
          msa_1hot,
          batch["extra_has_deletion"].unsqueeze(-1),
          batch["extra_deletion_value"].unsqueeze(-1),
      ]
      return torch.cat(msa_feat, dim=-1)
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/models/esm/openfold_utils/feats.py
imports: import torch
  import torch.nn
  from .None import residue_constants
  from .rigid_utils import Rigid, Rotation
  from .tensor_utils import batched_gather
label_desc: Use image node_id 5 for calling a global function with example usage: build_extra_msa_feat(batch) and returns: torch