docstr: Applies the given gradients to the network weights and resets the accumulation placeholder
  :param gradients: The gradients to use for the update
  :param scaler: A scaling factor that allows rescaling the gradients before applying them
  :param additional_inputs: optional additional inputs required for when applying the gradients (e.g. batchnorm's
                            update ops also requires the inputs)
code: def apply_and_reset_gradients(
      self, gradients, scaler=1.0, additional_inputs=None
  ):
      """
      Applies the given gradients to the network weights and resets the accumulation placeholder
      :param gradients: The gradients to use for the update
      :param scaler: A scaling factor that allows rescaling the gradients before applying them
      :param additional_inputs: optional additional inputs required for when applying the gradients (e.g. batchnorm's
                                update ops also requires the inputs)
  
      """
      self.apply_gradients(
          gradients, scaler, additional_inputs=additional_inputs
      )
      self.reset_accumulated_gradients()
  
imports: import os
  import time
  from typing import Any, List, Tuple, Dict
  import numpy
  import tensorflow
  from rl_coach.architectures.architecture import Architecture
  from rl_coach.architectures.tensorflow_components.savers import GlobalVariableSaver
  from rl_coach.base_parameters import AgentParameters, DistributedTaskParameters
  from rl_coach.core_types import GradientClippingMethod
  from rl_coach.saver import SaverCollection
  from rl_coach.spaces import SpacesDefinition
  from rl_coach.utils import force_list, squeeze_list, start_shell_command_and_wait
label_desc: Use image node_id 13 for calling the TensorFlowArchitecture obj's underlying member method code with example usage: obj.apply_and_reset_gradients(gradients, scaler, additional_inputs) without return types