code: def _build_module(self, input_layer):
      # NAF
      self.action = tf.placeholder(
          tf.float32, [None, self.num_actions], name="action"
      )
      self.input = self.action
  
      # V Head
      self.V = self.dense_layer(1)(input_layer, name="V")
  
      # mu Head
      mu_unscaled = self.dense_layer(self.num_actions)(
          input_layer,
          activation=self.activation_function,
          name="mu_unscaled",
      )
      self.mu = tf.multiply(mu_unscaled, self.output_scale, name="mu")
  
      # A Head
      # l_vector is a vector that includes a lower-triangular matrix values
      self.l_vector = self.dense_layer(
          (self.num_actions * (self.num_actions + 1)) / 2
      )(input_layer, name="l_vector")
  
      # Convert l to a lower triangular matrix and exponentiate its diagonal
  
      i = 0
      columns = []
      for col in range(self.num_actions):
          start_row = col
          num_non_zero_elements = self.num_actions - start_row
          zeros_column_part = tf.zeros_like(
              self.l_vector[:, 0:start_row]
          )
          diag_element = tf.expand_dims(tf.exp(self.l_vector[:, i]), 1)
          non_zeros_non_diag_column_part = self.l_vector[
              :, (i + 1) : (i + num_non_zero_elements)
          ]
          columns.append(
              tf.concat(
                  [
                      zeros_column_part,
                      diag_element,
                      non_zeros_non_diag_column_part,
                  ],
                  axis=1,
              )
          )
          i += num_non_zero_elements
      self.L = tf.transpose(tf.stack(columns, axis=1), (0, 2, 1))
  
      # P = L*L^T
      self.P = tf.matmul(self.L, tf.transpose(self.L, (0, 2, 1)))
  
      # A = -1/2 * (u - mu)^T * P * (u - mu)
      action_diff = tf.expand_dims(self.action - self.mu, -1)
      a_matrix_form = -0.5 * tf.matmul(
          tf.transpose(action_diff, (0, 2, 1)),
          tf.matmul(self.P, action_diff),
      )
      self.A = tf.reshape(a_matrix_form, [-1, 1])
  
      # Q Head
      self.Q = tf.add(self.V, self.A, name="Q")
  
      self.output = self.Q
  
file_path: coach/rl_coach/architectures/tensorflow_components/heads/naf_head.py
imports: import tensorflow
  from rl_coach.architectures.tensorflow_components.layers import Dense
  from rl_coach.architectures.tensorflow_components.heads.head import Head
  from rl_coach.base_parameters import AgentParameters
  from rl_coach.core_types import QActionStateValue
  from rl_coach.spaces import BoxActionSpace
  from rl_coach.spaces import SpacesDefinition
label_desc: Use image node_id 2 for calling the NAFHead obj's underlying member method code with example usage: obj._build_module(input_layer) without return types