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)
code: def __init__(
      self,
      input_dims: Union[int, List[int], torch.Size],
      device: Optional[torch.device] = None,
  ) -> None:
      super().__init__()
      self.norm: torch.nn.modules.Sequential = nn.Sequential(
          nn.LayerNorm(input_dims, device=device),
          nn.Sigmoid(),
      )
  
imports: from typing import List, Optional, Union
  import torch
  from torch import nn
label_desc: Use image node_id 1 to create a new SwishLayerNorm object from inherited base classes: nn with example: obj = SwishLayerNorm(input_dims, device)