code: def nplr(measure, n, rank=1, dtype=torch.float):
      """
      Return w, p, q, v, b such that
      (w - p q^*, b) is unitarily equivalent to the original HiPPO a, b by the matrix v
      i.e. a = v[w - p q^*]v^*, b = v b
      Args:
          measure (str): the type of measure
              legt - Legendre (translated)
              legs - Legendre (scaled)
              glagt - generalized Laguerre (translated)
              lagt, tlagt - previous versions of (tilted) Laguerre with slightly different normalization
          n (int): dimension
          rank (int): rank
          dtype (str): data type
      """
      a, b = transition(measure, n)
      a = torch.as_tensor(a, dtype=dtype)  # (n, n)
      b = torch.as_tensor(b, dtype=dtype)[:, 0]  # (n,)
  
      p = rank_correction(measure, n, rank=rank, dtype=dtype)
      ap = a + torch.sum(p.unsqueeze(-2) * p.unsqueeze(-1), dim=-3)
      w, v = torch.linalg.eig(ap)  # (..., n) (..., n, n)
      # v w v^{-1} = A
  
      # Only keep one of the conjugate pairs
      w = w[..., 0::2].contiguous()
      v = v[..., 0::2].contiguous()
  
      v_inv = v.conj().transpose(-1, -2)
  
      b = contract("ij, j -> i", v_inv, b.to(v))  # v^* b
      p = contract("ij, ...j -> ...i", v_inv, p.to(v))  # v^* p
  
      return w, p, p, b, v
  
file_path: towhee/towhee/models/vis4mer/utils.py
imports: import torch
  from einops import rearrange
  from scipy import special
  import numpy
  from opt_einsum import contract
  from pykeops.torch import Genred
label_desc: Use image node_id 7 for calling a global function with example usage: nplr(measure, n, rank, dtype) and returns: w,  p,  p,  b,  v