code: def __init__(
      self,
      max_feature_lengths: Dict[str, int],
      device: Optional[torch.device] = None,
  ) -> None:
      super().__init__()
      self.max_feature_lengths = max_feature_lengths
      for length in self.max_feature_lengths.values():
          if length <= 0:
              raise
      self.position_weights: nn.ParameterDict = nn.ParameterDict()
      for key, length in max_feature_lengths.items():
          self.position_weights[key] = nn.Parameter(
              torch.empty([length], device=device).fill_(1.0)
          )
      self.register_buffer(
          "_dummy_weights",
          torch.tensor(
              max(self.max_feature_lengths.values()),
              device=device,
          ).fill_(1.0),
      )
  
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 1 to create a new GroupedPositionWeightedModule object from inherited base classes: BaseGroupedFeatureProcessor with example: obj = GroupedPositionWeightedModule(max_feature_lengths, device)