class_docstr: Adds position weights to id list features.
  
  Args:
      `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`.
class_docstr_tok: ["Adds","position","weights","to","id","list","features",".","Args",":","`","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():
          self.position_weight.fill_(1.0)
  
imports: import abc
  from typing import Dict, Optional
  import torch
  from torch import nn
  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