code: def state_dict(
      self,
      destination: Optional[Dict[str, Any]] = None,
      prefix: str = "",
      keep_vars: bool = False,
  ) -> Dict[str, Any]:
      if destination is None:
          destination = OrderedDict()
          # pyre-ignore [16]
          destination._metadata = OrderedDict()
      for name, param in self.position_weights.items():
          destination[prefix + f"position_weights.{name}"] = (
              param if keep_vars else param.detach()
          )
      return destination
  
file_path: torchrec/torchrec/distributed/grouped_position_weighted.py
imports: from collections import OrderedDict
  from typing import Any, Dict, Iterator, Optional, Tuple
  import torch
  import torch.nn
  from torchrec.distributed.embedding_types import BaseGroupedFeatureProcessor
  from torchrec.distributed.utils import append_prefix
  from torchrec.sparse.jagged_tensor import KeyedJaggedTensor
label_desc: Use image node_id 5 for calling the GroupedPositionWeightedModule obj's underlying member method code with example usage: obj.state_dict(destination, prefix, keep_vars) and returns: destination