code: def add_residual(
      x, brange, residual, residual_scale_factor, scaling_vector=None
  ):
      if scaling_vector is None:
          x_flat = x.flatten(1)
          residual = residual.flatten(1)
          x_plus_residual = torch.index_add(
              x_flat,
              0,
              brange,
              residual.to(dtype=x.dtype),
              alpha=residual_scale_factor,
          )
      else:
          x_plus_residual = scaled_index_add(
              x,
              brange,
              residual.to(dtype=x.dtype),
              scaling=scaling_vector,
              alpha=residual_scale_factor,
          )
      return x_plus_residual
  
imports: import logging
  import os
  from typing import Callable, List, Any, Tuple, Dict
  import warnings
  import torch
  from torch import nn, Tensor
  from .attention import Attention, MemEffAttention
  from .drop_path import DropPath
  from .layer_scale import LayerScale
  from .mlp import Mlp
label_desc: Use image node_id 3 for calling a global function with example usage: add_residual(x, brange, residual, residual_scale_factor, scaling_vector) and returns: x_plus_residual