docstr: Applies the given gradients to the network weights and resets accumulated gradients to zero
  :param gradients: The gradients to use for the update
  :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","accumulated","gradients","to","zero",":","param","gradients",":","The","gradients","to","use","for","the","update",":","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 accumulated gradients to zero
      :param gradients: The gradients to use for the update
      :param scaler: A scaling factor that allows rescaling the gradients before applying them
      """
      self.apply_gradients(gradients, scaler)
      self.reset_accumulated_gradients()
  
imports: import copy
  from typing import Any, Dict, Generator, List, Tuple, Union
  import numpy
  import mxnet
  from mxnet import autograd, gluon, nd
  from mxnet.ndarray import NDArray
  from rl_coach.architectures.architecture import Architecture
  from rl_coach.architectures.mxnet_components.heads.head import LOSS_OUT_TYPE_LOSS, LOSS_OUT_TYPE_REGULARIZATION
  from rl_coach.architectures.mxnet_components import utils
  from rl_coach.architectures.mxnet_components.savers import ParameterDictSaver, OnnxSaver
  from rl_coach.base_parameters import AgentParameters
  from rl_coach.logger import screen
  from rl_coach.saver import SaverCollection
  from rl_coach.spaces import SpacesDefinition
  from rl_coach.utils import force_list, squeeze_list
label_desc: Use image node_id 11 for calling the MxnetArchitecture obj's underlying member method code with example usage: obj.apply_and_reset_gradients(gradients, scaler) without return types