code: def drop_add_residual_stochastic_depth_list(
      x_list: List[Tensor],
      residual_func: Callable[[Tensor, Any], Tensor],
      sample_drop_ratio: float = 0.0,
      scaling_vector=None,
  ) -> Tensor:
      # 1) generate random set of indices for dropping samples in the batch
      branges_scales = [
          get_branges_scales(x, sample_drop_ratio=sample_drop_ratio)
          for x in x_list
      ]
      branges = [s[0] for s in branges_scales]
      residual_scale_factors = [s[1] for s in branges_scales]
  
      # 2) get attention bias and index+concat the tensors
      attn_bias, x_cat = get_attn_bias_and_cat(x_list, branges)
  
      # 3) apply residual_func to get residual, and split the result
      residual_list = attn_bias.split(residual_func(x_cat, attn_bias=attn_bias))  # type: ignore
  
      outputs = []
      for x, brange, residual, residual_scale_factor in zip(
          x_list, branges, residual_list, residual_scale_factors
      ):
          outputs.append(
              add_residual(
                  x,
                  brange,
                  residual,
                  residual_scale_factor,
                  scaling_vector,
              ).view_as(x)
          )
      return outputs
  
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 5 for calling a global function with example usage: drop_add_residual_stochastic_depth_list(x_list, residual_func, sample_drop_ratio, scaling_vector) and returns: outputs