code: def combination(measures, N, R, S, **ssm_args):
      if isinstance(measures, str):
          measures = (
              combinations[measures]
              if measures in combinations
              else [measures]
          )
  
      assert S % len(measures) == 0, (
          f"{S} independent trainable SSM copies must be multiple of {len(measures)} "
          "different measures"
      )
      w, P, B, V = zip(
          *[
              ssm(measure, N, R, S // len(measures), **ssm_args)
              for measure in measures
          ]
      )
      w = torch.cat(w, dim=0)  # (S N)
      P = torch.cat(P, dim=1)  # (R S N)
      B = torch.cat(B, dim=0)  # (S N)
      V = torch.cat(V, dim=0)  # (S N N)
      return w, P, B, V
  
imports: import logging
  import math
  import os
  from functools import wraps
  from typing import Any, Callable, Optional
  import numpy
  import opt_einsum
  import torch
  import torch.nn
  import torch.nn.functional
  from einops import rearrange, repeat
  from espnet2.asr.state_spaces.components import Activation, DropoutNd, LinearActivation
label_desc: Use image node_id 11 for calling a global function with example usage: combination(measures, N, R, S) and returns: w,  P,  B,  V