code: def _build_module(self, input_layer):
      # Standard V Network
      q_outputs = []
      self.target = tf.placeholder(
          tf.float32, shape=(None, 1), name="q_networks_min_placeholder"
      )
  
      for i in range(
          input_layer.shape[0]
      ):  # assuming that the actual size is 2, as there are two critic networks
          if self.initializer == "normalized_columns":
              q_outputs.append(
                  self.dense_layer(1)(
                      input_layer[i],
                      name="q_output_{}".format(i + 1),
                      kernel_initializer=normalized_columns_initializer(
                          1.0
                      ),
                      bias_initializer=self.output_bias_initializer,
                  ),
              )
          elif self.initializer == "xavier" or self.initializer is None:
              q_outputs.append(
                  self.dense_layer(1)(
                      input_layer[i],
                      name="q_output_{}".format(i + 1),
                      bias_initializer=self.output_bias_initializer,
                  )
              )
  
          self.output.append(q_outputs[i])
          self.loss.append(
              tf.reduce_mean((self.target - q_outputs[i]) ** 2)
          )
  
      self.output.append(tf.reduce_min(q_outputs, axis=0))
      self.output.append(tf.reduce_mean(self.output[0]))
      self.loss = sum(self.loss)
      tf.losses.add_loss(self.loss)
  
file_path: coach/rl_coach/architectures/tensorflow_components/heads/td3_v_head.py
imports: import tensorflow
  from rl_coach.architectures.tensorflow_components.layers import Dense
  from rl_coach.architectures.tensorflow_components.heads.head import Head, normalized_columns_initializer
  from rl_coach.base_parameters import AgentParameters
  from rl_coach.core_types import VStateValue
  from rl_coach.spaces import SpacesDefinition
label_desc: Use image node_id 2 for calling the TD3VHead obj's underlying member method code with example usage: obj._build_module(input_layer) without return types