class_docstr: Applies the Swish function with layer normalization: `Y = X * Sigmoid(LayerNorm(X))`.
  
  Args:
      input_dims (Union[int, List[int], torch.Size]): dimensions to normalize over.
          If an input tensor has shape [batch_size, d1, d2, d3], setting
          input_dim=[d2, d3] will do the layer normalization on last two dimensions.
      device (Optional[torch.device]): default compute device.
  
  Example::
  
      sln = SwishLayerNorm(100)
docstr: Args:
      input (torch.Tensor): an input tensor.
  
  Returns:
      torch.Tensor: an output tensor.
docstr_tok: ["Args",":","input","(","torch.Tensor",")",":","an","input","tensor",".","Returns",":","torch.Tensor",":","an","output","tensor","."]
code: def forward(
      self,
      input: torch.Tensor,
  ) -> torch.Tensor:
      """
      Args:
          input (torch.Tensor): an input tensor.
  
      Returns:
          torch.Tensor: an output tensor.
      """
      return input * self.norm(input)
  
imports: from typing import List, Optional, Union
  import torch
  from torch import nn
label_desc: Use image node_id 2 for calling the SwishLayerNorm obj's underlying member method code with example usage: obj.forward(input) and returns: unknown