class_docstr: Class that performs the main optimization.
  Keeps track of the current x and iterates through data to learn x given the penalty and order.
class_docstr_tok: ["Class","that","performs","the","main","optimization",".","Keeps","track","of","the","current","x","and","iterates","through","data","to","learn","x","given","the","penalty","and","order","."]
docstr: Returns boolean of whether to skip the currrent y if there is nothing to be learned from it.
docstr_tok: ["Returns","boolean","of","whether","to","skip","the","currrent","y","if","there","is","nothing","to","be","learned","from","it","."]
code: def _skip_y_forward(self, y):
      """
      Returns boolean of whether to skip the currrent y if there is nothing to be learned from it.
      """
      if y is None:
          return True
      elif torch.unique(y).size()[0] < 2:
          return True
      else:
          return False
  
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 8 for calling the Solver obj's underlying member method code with example usage: obj._skip_y_forward(y) and returns: True, True, False