class_docstr: Mish activation definition.
  
  Mish(x) = x * tanh(softplus(x))
  
  Reference: https://arxiv.org/abs/1908.08681.
  
  Args:
      softplus_beta: Beta value for softplus activation formulation.
                       (Usually 0 > softplus_beta >= 2)
      softplus_threshold: Values above this revert to a linear function.
                       (Usually 10 > softplus_threshold >= 20)
      use_builtin: Whether to use PyTorch activation function if available.
class_docstr_tok: ["Mish","activation","definition",".","Mish","(","x",")","=","x","*","tanh","(","softplus","(","x",")",")","Reference",":","https",":","\/\/arxiv.org\/abs\/1908.08681",".","Args",":","softplus_beta",":","Beta","value","for","softplus","activation","formulation",".","(","Usually","0",">","softplus_beta",">","=","2",")","softplus_threshold",":","Values","above","this","revert","to","a","linear","function",".","(","Usually","10",">","softplus_threshold",">","=","20",")","use_builtin",":","Whether","to","use","PyTorch","activation","function","if","available","."]
docstr: Forward computation.
docstr_tok: ["Forward","computation","."]
code: def forward(self, x: torch.Tensor) -> torch.Tensor:
      """Forward computation."""
      return self.mish(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 Mish obj's underlying member method code with example usage: obj.forward(x) and returns: self