code: def ssm(measure, N, R, H, **ssm_args):
      """Dispatcher to create single SSM initialization.
  
      N: state size
      R: rank (for DPLR parameterization)
      H: number of independent SSM copies
      """
      if measure == "dplr":
          w, P, B, V = dplr(N=N, rank=R, H=H, **ssm_args)
      elif measure.startswith("diag"):
          args = measure.split("-")
          assert args[0] == "diag" and len(args) > 1
          scaling = args[1]
          w, P, B, V = dplr(
              scaling=scaling,
              N=N,
              rank=R,
              H=H,
              diagonal=True,
              **ssm_args,
          )
      else:
          w, P, B, V = nplr(measure, N, R, **ssm_args)
          w = repeat(w, "n -> s n", s=H)
          P = repeat(P, "r n -> r s n", s=H)
          B = repeat(B, "n -> s n", s=H)
          V = repeat(V, "n m -> s n m", s=H)
      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 10 for calling a global function with example usage: ssm(measure, N, R, H) and returns: w,  P,  B,  V