class_docstr: Gradient accumulation utility. When used with a distribution strategy, the accumulator should be called in a
  replica context. Gradients will be accumulated locally on each replica and without synchronization. Users should
  then call `.gradients`, scale the gradients if required, and pass the result to `apply_gradients`.
class_docstr_tok: ["Gradient","accumulation","utility",".","When","used","with","a","distribution","strategy",",","the","accumulator","should","be","called","in","a","replica","context",".","Gradients","will","be","accumulated","locally","on","each","replica","and","without","synchronization",".","Users","should","then","call","`",".gradients","`",",","scale","the","gradients","if","required",",","and","pass","the","result","to","`","apply_gradients","`","."]
docstr: Resets the accumulated gradients on the current replica.
docstr_tok: ["Resets","the","accumulated","gradients","on","the","current","replica","."]
code: def reset(self):
      """Resets the accumulated gradients on the current replica."""
      if not self._gradients:
          return
      self._accum_steps.assign(0)
      for gradient in self._gradients:
          if gradient is not None:
              gradient.assign(tf.zeros_like(gradient))
  
imports: import re
  from typing import Callable, List, Optional, Union
  import tensorflow
label_desc: Use image node_id 5 for calling the GradientAccumulator obj's underlying member method code with example usage: obj.reset() without return types