code: def dplr(
      scaling,
      N,
      rank=1,
      H=1,
      dtype=torch.float,
      real_scale=1.0,
      imag_scale=1.0,
      random_real=False,
      random_imag=False,
      normalize=False,
      diagonal=True,
      random_B=False,
  ):
      assert dtype == torch.float or torch.double
      dtype = torch.cfloat if dtype == torch.float else torch.cdouble
  
      pi = torch.tensor(math.pi)
      if random_real:
          real_part = torch.rand(H, N // 2)
      else:
          real_part = 0.5 * torch.ones(H, N // 2)
      if random_imag:
          imag_part = N // 2 * torch.rand(H, N // 2)
      else:
          imag_part = repeat(torch.arange(N // 2), "n -> h n", h=H)
  
      real_part = real_scale * real_part
      if scaling == "random":
          imag_part = torch.randn(H, N // 2)
      elif scaling == "real":
          imag_part = 0 * imag_part
          real_part = 1 + repeat(torch.arange(N // 2), "n -> h n", h=H)
      elif scaling in ["linear", "lin"]:
          imag_part = pi * imag_part
      elif scaling in [
          "inverse",
          "inv",
      ]:  # Based on asymptotics of the default HiPPO matrix
          imag_part = 1 / pi * N * (N / (1 + 2 * imag_part) - 1)
      elif scaling in ["inverse2", "inv2"]:
          imag_part = 1 / pi * N * (N / (1 + imag_part) - 1)
      elif scaling in ["quadratic", "quad"]:
          imag_part = 1 / pi * (1 + 2 * imag_part) ** 2
      elif scaling in ["legs", "hippo"]:
          w, _, _, _ = nplr("legsd", N)
          imag_part = w.imag
  
      else:
          raise NotImplementedError
      imag_part = imag_scale * imag_part
      w = -real_part + 1j * imag_part
  
      # Initialize B
      if random_B:
          B = torch.randn(H, N // 2, dtype=dtype)
      else:
          B = torch.ones(H, N // 2, dtype=dtype)
  
      if normalize:
          norm = (
              -B / w
          )  # (H, N) # Result if you integrate the kernel with constant 1 function
          zeta = 2 * torch.sum(
              torch.abs(norm) ** 2, dim=-1, keepdim=True
          )  # Variance with a random C vector
          B = B / zeta**0.5
  
      P = torch.randn(rank, H, N // 2, dtype=dtype)
      if diagonal:
          P = P * 0.0
      V = torch.eye(N, dtype=dtype)[:: N // 2]  # Only used in testing
      V = repeat(V, "n m -> h n m", h=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 9 for calling a global function with example usage: dplr(scaling, N, rank, H, dtype, real_scale, imag_scale, random_real, random_imag, normalize, diagonal, random_B) and returns: w,  P,  B,  V