class_docstr: Transformer with extra features.
  
  This module is the backbone of `FTTransformer`.
class_docstr_tok: ["Transformer","with","extra","features",".","This","module","is","the","backbone","of","`","FTTransformer","`","."]
code: def _start_residual(self, layer, stage, x):
      assert stage in ["attention", "ffn"], _INTERNAL_ERROR_MESSAGE
      x_residual = x
      if self.prenormalization:
          norm_key = f"{stage}_normalization"
          if norm_key in layer:
              x_residual = layer[norm_key](x_residual)
      return x_residual
  
imports: import enum
  import math
  import warnings
  from typing import Callable, Dict, List, Optional, Tuple, Union, cast
  import torch
  import torch.nn.functional
  from torch import Tensor, nn
  from .utils import init_weights
label_desc: Use image node_id 3 for calling the Custom_Transformer obj's underlying member method code with example usage: obj._start_residual(layer, stage, x) and returns: x_residual