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","."]
docstr: Args:
      verbose (bool): whether to print warning about None gradients.
docstr_tok: ["Args",":","verbose","(","bool",")",":","whether","to","print","warning","about","None","gradients","."]
code: def __init__(self, verbose=True):
      """
      Args:
          verbose (bool): whether to print warning about None gradients.
      """
      super(FilterNoneGrad, self).__init__()
      self._verbose = verbose
  
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 1 to create a new FilterNoneGrad object from inherited base classes: GradientProcessor with example: obj = FilterNoneGrad(verbose)