class_docstr: The class implements the Swish activation function from
  https://arxiv.org/pdf/2005.03191.pdf
  
  given input x. Swish(x) = x / (1 + exp(beta * x))
  
  Arguments
  ---------
  beta: float
      Beta value.
  
  Example
  -------
  >>> x = torch.randn((8, 40, 120))
  >>> act = Swish()
  >>> x = act(x)
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docstr: Returns the Swished input tensor.
  
  Arguments
  ---------
  x : torch.Tensor
      Input tensor.
docstr_tok: ["Returns","the","Swished","input","tensor",".","Arguments","--","--","--","--","-","x",":","torch.Tensor","Input","tensor","."]
code: def forward(self, x):
      """Returns the Swished input tensor.
  
      Arguments
      ---------
      x : torch.Tensor
          Input tensor.
      """
      return x * self.sigmoid(self.beta * x)
  
file_path: speechbrain/speechbrain/nnet/activations.py
imports: import torch
  import logging
  import torch.nn.functional
label_desc: Use image node_id 2 for calling the Swish obj's underlying member method code with example usage: obj.forward(x) and returns: unknown