code: def __init__(
      self,
      agent_parameters: AgentParameters,
      spaces: SpacesDefinition,
      network_name: str,
      head_idx: int = 0,
      loss_weight: float = 1.0,
      is_local: bool = True,
      activation_function: str = "relu",
      dense_layer=Dense,
  ):
      super().__init__(
          agent_parameters,
          spaces,
          network_name,
          head_idx,
          loss_weight,
          is_local,
          activation_function,
          dense_layer=dense_layer,
      )
      self.name = "dnd_q_values_head"
      self.DND_size = agent_parameters.algorithm.dnd_size
      self.DND_key_error_threshold = (
          agent_parameters.algorithm.DND_key_error_threshold
      )
      self.l2_norm_added_delta = (
          agent_parameters.algorithm.l2_norm_added_delta
      )
      self.new_value_shift_coefficient = (
          agent_parameters.algorithm.new_value_shift_coefficient
      )
      self.number_of_nn = agent_parameters.algorithm.number_of_knn
      self.ap = agent_parameters
      self.dnd_embeddings = [None] * self.num_actions
      self.dnd_values = [None] * self.num_actions
      self.dnd_indices = [None] * self.num_actions
      self.dnd_distances = [None] * self.num_actions
      if self.ap.memory.shared_memory:
          self.shared_memory_scratchpad = (
              self.ap.task_parameters.shared_memory_scratchpad
          )
  
file_path: coach/rl_coach/architectures/tensorflow_components/heads/dnd_q_head.py
imports: import tensorflow
  from rl_coach.architectures.tensorflow_components.layers import Dense
  from rl_coach.architectures.tensorflow_components.heads.q_head import QHead
  from rl_coach.base_parameters import AgentParameters
  from rl_coach.memories.non_episodic import differentiable_neural_dictionary
  from rl_coach.spaces import SpacesDefinition
label_desc: Use image node_id 1 to create a new DNDQHead object from inherited base classes: QHead with example: obj = DNDQHead(agent_parameters, spaces, network_name, head_idx, loss_weight, is_local, activation_function, dense_layer)