code: def th_matrixcorr(x, y):
      """
      return a correlation matrix between
      columns of x and columns of y.
  
      So, if X.size() == (1000,4) and Y.size() == (1000,5),
      then the result will be of size (4,5) with the
      (i,j) value equal to the pearsonr correlation coeff
      between column i in X and column j in Y
      """
      mean_x = th.mean(x, 0)
      mean_y = th.mean(y, 0)
      xm = x.sub(mean_x.expand_as(x))
      ym = y.sub(mean_y.expand_as(y))
      r_num = xm.t().mm(ym)
      r_den1 = th.norm(xm, 2, 0)
      r_den2 = th.norm(ym, 2, 0)
      r_den = r_den1.t().mm(r_den2)
      r_mat = r_num.div(r_den)
      return r_mat
  
imports: import pickle
  import random
  from .optimizers import *
  from .callbacks import *
  import torch
  from pywick.callbacks import *
label_desc: Use image node_id 20 for calling a global function with example usage: th_matrixcorr(x, y) and returns: r_mat