class_docstr: Applies elementwise activation based on Laplace function, introduced in MEGA as an attention activation. See
  https://arxiv.org/abs/2209.10655
  
  Inspired by squared relu, but with bounded range and gradient for better stability
class_docstr_tok: ["Applies","elementwise","activation","based","on","Laplace","function",",","introduced","in","MEGA","as","an","attention","activation",".","See","https",":","\/\/arxiv.org\/abs\/2209.10655","Inspired","by","squared","relu",",","but","with","bounded","range","and","gradient","for","better","stability"]
code: def forward(self, input, mu=0.707107, sigma=0.282095):
      input = (input - mu).div(sigma * math.sqrt(2.0))
      return 0.5 * (1.0 + torch.erf(input))
  
imports: import math
  import warnings
  from collections import OrderedDict
  import torch
  from packaging import version
  from torch import Tensor, nn
  from .utils import logging
label_desc: Use image node_id 1 for calling the LaplaceActivation obj's underlying member method code with example usage: obj.forward(input, mu, sigma) and returns: unknown