docstr: minimize the gradient loss
  
  :param loss: ([TensorFlow Tensor]) The loss
  :param loss_sampled: ([TensorFlow Tensor]) the loss function output
  :param var_list: ([TensorFlow Tensor]) The parameters
  :return: (function, q_runner) train operation, queue operation runner
docstr_tok: ["minimize","the","gradient","loss",":","param","loss",":","(","[","TensorFlow","Tensor","]",")","The","loss",":","param","loss_sampled",":","(","[","TensorFlow","Tensor","]",")","the","loss","function","output",":","param","var_list",":","(","[","TensorFlow","Tensor","]",")","The","parameters",":","return",":","(","function",",","q_runner",")","train","operation",",","queue","operation","runner"]
code: def minimize(self, loss, loss_sampled, var_list=None):
      """
      minimize the gradient loss
  
      :param loss: ([TensorFlow Tensor]) The loss
      :param loss_sampled: ([TensorFlow Tensor]) the loss function output
      :param var_list: ([TensorFlow Tensor]) The parameters
      :return: (function, q_runner) train operation, queue operation runner
      """
      grads = self.compute_gradients(loss, var_list=var_list)
      self.compute_and_apply_stats(loss_sampled, var_list=var_list)
      return self.apply_gradients(grads)
  
imports: import re
  from functools import reduce
  import tensorflow
  import numpy
  from stable_baselines.acktr.kfac_utils import detect_min_val, factor_reshape, gmatmul
label_desc: Use image node_id 15 for calling the KfacOptimizer obj's underlying member method code with example usage: obj.minimize(loss, loss_sampled, var_list) and returns: self