class_docstr: Smish activation definition.
  
  Smish(x) = (alpha * x) * tanh(log(1 + sigmoid(beta * x)))
               where alpha > 0 and beta > 0
  
  Reference: https://www.mdpi.com/2079-9292/11/4/540/htm.
  
  Args:
      alpha: Alpha value for Smish activation fomulation.
               (Usually, alpha = 1. If alpha <= 0, set value to 1).
      beta: Beta value for Smish activation formulation.
              (Usually, beta = 1. If beta <= 0, set value to 1).
class_docstr_tok: ["Smish","activation","definition",".","Smish","(","x",")","=","(","alpha","*","x",")","*","tanh","(","log","(","1","+","sigmoid","(","beta","*","x",")",")",")","where","alpha",">","0","and","beta",">","0","Reference",":","https",":","\/\/www.mdpi.com\/2079-9292\/11\/4\/540\/htm",".","Args",":","alpha",":","Alpha","value","for","Smish","activation","fomulation",".","(","Usually",",","alpha","=","1",".","If","alpha","<","=","0",",","set","value","to","1",")",".","beta",":","Beta","value","for","Smish","activation","formulation",".","(","Usually",",","beta","=","1",".","If","beta","<","=","0",",","set","value","to","1",")","."]
docstr: Forward computation.
docstr_tok: ["Forward","computation","."]
code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      """Forward computation."""
      return self.smish(x)
  
file_path: espnet/espnet2/asr_transducer/activation.py
imports: import torch
  from packaging.version import parse
label_desc: Use image node_id 2 for calling the Smish obj's underlying member method code with example usage: obj.forward(x) and returns: self