class_docstr: Mish: A Self Regularized Non-Monotonic Neural Activation Function - https://arxiv.org/abs/1908.08681
  Experimental memory-efficient variant
class_docstr_tok: ["Mish",":","A","Self","Regularized","Non-Monotonic","Neural","Activation","Function","-","https",":","\/\/arxiv.org\/abs\/1908.08681","Experimental","memory-efficient","variant"]
code: def forward(ctx, x):
      ctx.save_for_backward(x)
      y = x.mul(torch.tanh(F.softplus(x)))  # x * tanh(ln(1 + exp(x)))
      return y
  
imports: import torch
  from torch import nn
  from torch.nn import functional
label_desc: Use image node_id 1 for calling the MishAutoFn obj's underlying member method code with example usage: obj.forward(ctx, x) and returns: y