code: from torch import nn
  
  from tsl.nn.utils import utils
  
  
  class Dense(nn.Module):
      r"""
      A simple fully-connected layer.
  
      Args:
          input_size (int): Size of the input.
          output_size (int): Size of the output.
          activation (str, optional): Activation function.
          dropout (float, optional): Dropout rate.
          bias (bool, optional): Whether to use a bias.
      """
      def __init__(self, input_size, output_size, activation='linear', dropout=0., bias=True):
          super(Dense, self).__init__()
          self.layer = nn.Sequential(
              nn.Linear(input_size, output_size, bias=bias),
              utils.get_layer_activation(activation)(),
              nn.Dropout(dropout) if dropout > 0. else nn.Identity()
          )
  
      def forward(self, x):
          return self.layer(x)
  
apis: torch.nn.Linear torch.nn.Dropout torch.nn.Identity