code: def rank_correction(measure, n, rank=1, dtype=torch.float):
      """
      Return low-rank matrix L such that A + L is normal
      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
      """
  
      if measure == "legs":
          assert rank >= 1
          p = torch.sqrt(0.5 + torch.arange(n, dtype=dtype)).unsqueeze(
              0
          )  # (1 n)
      elif measure == "legt":
          assert rank >= 2
          p = torch.sqrt(1 + 2 * torch.arange(n, dtype=dtype))  # (n)
          p0 = p.clone()
          p0[0::2] = 0.0
          p1 = p.clone()
          p1[1::2] = 0.0
          p = torch.stack([p0, p1], dim=0)  # (2 n)
      elif measure == "lagt":
          assert rank >= 1
          p = 0.5**0.5 * torch.ones(1, n, dtype=dtype)
      else:
          raise NotImplementedError
  
      d = p.size(0)
      if rank > d:
          p = torch.stack(
              [p, torch.zeros(n, dtype=dtype).repeat(rank - d, d)],
              dim=0,
          )  # (rank n)
      return p
  
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 6 for calling a global function with example usage: rank_correction(measure, n, rank, dtype) and returns: p