class_docstr: Adds position weights to id list features.
  
  Args:
      max_feature_lengths (Dict[str, int]): feature name to `max_length` mapping.
          `max_length`, a.k.a truncation size, specifies the maximum number of ids
          each sample has. For each feature, its position weight parameter size is
          `max_length`.
code: def reset_parameters(self) -> None:
      with torch.no_grad():
          for key, _length in self.max_feature_lengths.items():
              self.position_weights[key].fill_(1.0)
  
imports: import abc
  from collections import OrderedDict
  from typing import Any, Dict, Iterator, List, Optional, Tuple
  import torch
  import torch.nn
  from torchrec.fx.tracer import is_fx_tracing
  from torchrec.sparse.jagged_tensor import JaggedTensor, KeyedJaggedTensor
label_desc: Use image node_id 2 for calling the PositionWeightedModule obj's underlying member method code with example usage: obj.reset_parameters() without return types