text: import timm
  import torch.nn as nn
  import torch.optim
  
  
  class FakeTransformer(nn.Module):
      """Basic transformer model"""
  
      def __init__(self, input_size: int = 128, output_size: int = 1000, device="cpu"):
          """
          Dummy example of __init__ function of basic transformer model. Does nothing as transformer is not even implemented.
          
          Args:
              input_size (int, optional): Input size fot the transformer model. Should be equal to the output size of the feature_extrator. Defaults to 128.
              output_size (int, optional): Output size. Should be equal to the selected representation size. Defaults to 1000.
          """
          super().__init__()
          self.__fully_connected = nn.Linear(input_size, output_size)
          self.__output_layer = nn.Softmax(dim=1)
          # this is a dummy example but in practice this will be longer
  
      def forward(self, input, **kwargs):
          x = self.__fully_connected(input)
          x = self.__output_layer(x)
          return x
  