docstr: Applies the given gradients to the network weights and resets the gradient accumulations.
  Has the same impact as calling `apply_gradients`, then `reset_accumulated_gradients`.
  
  :param gradients: gradients for the parameter weights, taken from `accumulated_gradients` property
      of an identical network (either self or another identical network)
  :param scaler: A scaling factor that allows rescaling the gradients before applying them
docstr_tok: ["Applies","the","given","gradients","to","the","network","weights","and","resets","the","gradient","accumulations",".","Has","the","same","impact","as","calling","`","apply_gradients","`",",","then","`","reset_accumulated_gradients","`",".",":","param","gradients",":","gradients","for","the","parameter","weights",",","taken","from","`","accumulated_gradients","`","property","of","an","identical","network","(","either","self","or","another","identical","network",")",":","param","scaler",":","A","scaling","factor","that","allows","rescaling","the","gradients","before","applying","them"]
code: def apply_and_reset_gradients(
      self, gradients: List[np.ndarray], scaler: float = 1.0
  ) -> None:
      """
      Applies the given gradients to the network weights and resets the gradient accumulations.
      Has the same impact as calling `apply_gradients`, then `reset_accumulated_gradients`.
  
      :param gradients: gradients for the parameter weights, taken from `accumulated_gradients` property
          of an identical network (either self or another identical network)
      :param scaler: A scaling factor that allows rescaling the gradients before applying them
      """
      raise NotImplementedError
  
imports: from typing import Any, Dict, List, Tuple
  import numpy
  from rl_coach.base_parameters import AgentParameters
  from rl_coach.saver import SaverCollection
  from rl_coach.spaces import SpacesDefinition
label_desc: Use image node_id 10 for calling the Architecture obj's underlying member method code with example usage: obj.apply_and_reset_gradients(gradients, scaler) without return types