class_docstr: RelativePositionBias module definition.
  
  Args:
      max_positions: Maximum number of relative positions.
class_docstr_tok: ["RelativePositionBias","module","definition",".","Args",":","max_positions",":","Maximum","number","of","relative","positions","."]
docstr: Reset module parameters.
  
  Args:
      val: Initialization value.
      std: Standard deviation.
docstr_tok: ["Reset","module","parameters",".","Args",":","val",":","Initialization","value",".","std",":","Standard","deviation","."]
code: def reset_parameters(
      self, val: float = 0.0, std: float = 0.02
  ) -> None:
      """Reset module parameters.
  
      Args:
          val: Initialization value.
          std: Standard deviation.
  
      """
      torch.nn.init.normal_(
          self.relative_position_bias, mean=val, std=std
      )
  
file_path: espnet/espnet2/asr_transducer/decoder/modules/mega/positional_bias.py
imports: import math
  from typing import Tuple
  import torch
label_desc: Use image node_id 2 for calling the RelativePositionBias obj's underlying member method code with example usage: obj.reset_parameters(val, std) without return types