class_docstr: A Wrapper for tf.train.Optimizer.
  
  Automatically takes care of:
  - Gradient averaging for multi-GPU training.
  - Gradient accumulation for arbitrarily large minibatches.
  - Dynamic loss scaling and typecasts for FP16 training.
  - Ignoring corrupted gradients that contain NaNs/Infs.
  - Reporting statistics.
  - Well-chosen default settings.
class_docstr_tok: ["A","Wrapper","for","tf.train.Optimizer",".","Automatically","takes","care","of",":","-","Gradient","averaging","for","multi-GPU","training",".","-","Gradient","accumulation","for","arbitrarily","large","minibatches",".","-","Dynamic","loss","scaling","and","typecasts","for","FP16","training",".","-","Ignoring","corrupted","gradients","that","contain","NaNs\/Infs",".","-","Reporting","statistics",".","-","Well-chosen","default","settings","."]
docstr: Reset internal state of the underlying optimizer.
docstr_tok: ["Reset","internal","state","of","the","underlying","optimizer","."]
code: def reset_optimizer_state(self) -> None:
      """Reset internal state of the underlying optimizer."""
      tfutil.assert_tf_initialized()
      tfutil.run(
          [
              var.initializer
              for device in self._devices.values()
              for var in device.optimizer.variables()
          ]
      )
  
imports: import numpy
  import tensorflow
  from collections import OrderedDict
  from typing import List, Union
  from .None import autosummary
  from .None import tfutil
  from ..None import util
  from .tfutil import TfExpression, TfExpressionEx
label_desc: Use image node_id 5 for calling the Optimizer obj's underlying member method code with example usage: obj.reset_optimizer_state() without return types