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: Combine the learnability and L1 penalty.
docstr_tok: ["Combine","the","learnability","and","L1","penalty","."]
code: def combine_loss(self, f_train, pen):
      """
      Combine the learnability and L1 penalty.
      """
      return (
          (1 - self.w) * f_train.detach() + self.w * pen.detach()
      ) / self.accum_steps
  
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 5 for calling the Solver obj's underlying member method code with example usage: obj.combine_loss(f_train, pen) and returns: unknown