class_docstr: Calculates the learnability of a set of features.
  mini-batch version w/ "left" and "right" multiplies
class_docstr_tok: ["Calculates","the","learnability","of","a","set","of","features",".","mini-batch","version","w\/","``","left","''","and","``","right","''","multiplies"]
code: def forward(self, s, X, y):
      l = y.clone()
      r = y.clone()
      z = 0
  
      for i in range(self.order):
          if i % 2 == 0:
              Z = triudr(X, r)
              r = torch.mm(Z, s)
          else:
              Z = triudl(X, l)
              l = torch.mm(Z, s)
          if self.a[i] != 0:
              # same the computation if a[i] is 0
              p = torch.mm(l.t(), r)
              z += self.a[i] * p
      return self.ret_val(z)
  
imports: import time
  import numpy
  import scipy.special
  import torch
  import torch.nn
  from .None import constants
  from .None import syssettings
  from .fginitialize import ChunkDataLoader
label_desc: Use image node_id 3 for calling the LearnabilityMB obj's underlying member method code with example usage: obj.forward(s, X, y) and returns: self