code: def _build_policy_network(self):
      network = tf.keras.Sequential(
          [
              tf.keras.layers.InputLayer(
                  input_shape=self.observation_shape
              ),
              tf.keras.layers.Conv1D(
                  filters=64,
                  kernel_size=6,
                  padding="same",
                  activation="tanh",
              ),
              tf.keras.layers.MaxPooling1D(pool_size=2),
              tf.keras.layers.Conv1D(
                  filters=32,
                  kernel_size=3,
                  padding="same",
                  activation="tanh",
              ),
              tf.keras.layers.MaxPooling1D(pool_size=2),
              tf.keras.layers.Flatten(),
              tf.keras.layers.Dense(
                  self.n_actions, activation="sigmoid"
              ),
              tf.keras.layers.Dense(
                  self.n_actions, activation="softmax"
              ),
          ]
      )
  
      return network
  
file_path: tensortrade/tensortrade/agents/parallel/parallel_dqn_model.py
imports: from deprecated import deprecated
  import random
  import numpy
  import tensorflow
  from typing import Callable
label_desc: Use image node_id 2 for calling the ParallelDQNModel obj's underlying member method code with example usage: obj._build_policy_network() and returns: network