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 __init__(
      self,
      Nminibatch,
      D,
      coeff,
      groups=None,
      binary=False,
      device=constants.Device.CPU,
  ):
      super(LearnabilityMB, self).__init__()
  
      a = coeff / scipy.special.binom(
          Nminibatch, np.arange(coeff.size) + 2
      )
      self.order = a.size
      # pylint: disable=E1102
      self.a = torch.tensor(
          a, dtype=torch.get_default_dtype(), requires_grad=False
      )
      self.binary = binary
  
      self.a = self.a.to(device)
  
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 1 to create a new LearnabilityMB object from inherited base classes: nn with example: obj = LearnabilityMB(Nminibatch, D, coeff, groups, binary, device)