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 gradients from learnability and penalty
  
  Parameters
  ----------
  g1 : array-like
      gradient from learnability
  g2 : array-like
      gradient from penalty
docstr_tok: ["Combine","gradients","from","learnability","and","penalty","Parameters","--","--","--","--","--","g1",":","array-like","gradient","from","learnability","g2",":","array-like","gradient","from","penalty"]
code: def combine_gradient(self, g1, g2):
      """
      Combine gradients from learnability and penalty
  
      Parameters
      ----------
      g1 : array-like
          gradient from learnability
      g2 : array-like
          gradient from penalty
      """
      to_return = ((1 - self.w) * g1 + self.w * g2) / self.accum_steps
      if self.groups is not None:
          # each column will get a gradient
          # but we can only up or down groups, so the gradient for the group
          # should be the average of the gradients of the columns
          to_return_grouped = torch.zeros_like(self.x)
          for group in torch.unique(self.groups):
              to_return_grouped[self.groups == group] = to_return[
                  self.groups == group
              ].mean()
          to_return = to_return_grouped
      return to_return
  
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 4 for calling the Solver obj's underlying member method code with example usage: obj.combine_gradient(g1, g2) and returns: to_return