class_docstr: Skip the update and print a warning (instead of crashing),
  when the gradient of certain variable is None.
class_docstr_tok: ["Skip","the","update","and","print","a","warning","(","instead","of","crashing",")",",","when","the","gradient","of","certain","variable","is","None","."]
code: def _process(self, grads):
      g = []
      to_print = []
      for grad, var in grads:
          if grad is None:
              to_print.append(var.op.name)
          else:
              g.append((grad, var))
      if self._verbose and len(to_print):
          message = ", ".join(to_print)
          logger.warn(
              "No gradient w.r.t {} trainable variables: {}".format(
                  len(to_print), message
              )
          )
      return g
  
imports: import inspect
  import re
  from abc import ABCMeta, abstractmethod
  import six
  import tensorflow
  from ..compat import tfv1
  from ..utils import logger
  from .summary import add_moving_summary
  from .symbolic_functions import print_stat, rms
label_desc: Use image node_id 2 for calling the FilterNoneGrad obj's underlying member method code with example usage: obj._process(grads) and returns: g