code: def get_action(self, state: np.ndarray, **kwargs) -> int:
      threshold: float = kwargs.get("threshold", 0)
  
      rand = random.random()
  
      if rand < threshold:
          return np.random.choice(self.n_actions)
      else:
          logits = self.actor_network(state[None, :], training=False)
          return tf.squeeze(
              tf.squeeze(tf.random.categorical(logits, 1), axis=-1),
              axis=-1,
          )
  
file_path: tensortrade/tensortrade/agents/a2c_agent.py
imports: from deprecated import deprecated
  import random
  import numpy
  import tensorflow
  from collections import namedtuple
  from tensortrade.agents import Agent, ReplayMemory
  from datetime import datetime
label_desc: Use image node_id 7 for calling the A2CAgent obj's underlying member method code with example usage: obj.get_action(state) and returns: np, tf