docstr: take greedy actions
  Args:
      obs (np.ndarray):                (n_agents, obs_shape)
      available_actions (np.ndarray):  (n_agents, n_actions)
  Returns:
      actions (np.ndarray):            (n_agents, )
docstr_tok: ["take","greedy","actions","Args",":","obs","(","np.ndarray",")",":","(","n_agents",",","obs_shape",")","available_actions","(","np.ndarray",")",":","(","n_agents",",","n_actions",")","Returns",":","actions","(","np.ndarray",")",":","(","n_agents",",",")"]
code: def predict(self, obs, available_actions):
      """take greedy actions
      Args:
          obs (np.ndarray):                (n_agents, obs_shape)
          available_actions (np.ndarray):  (n_agents, n_actions)
      Returns:
          actions (np.ndarray):            (n_agents, )
      """
      obs = paddle.to_tensor(obs, dtype="float32")
      available_actions = paddle.to_tensor(
          available_actions, dtype="int32"
      )
      agents_q, self.alg.hidden_states = self.alg.predict_local_q(
          obs, self.alg.hidden_states
      )
      # mask unavailable actions
      unavailable_actions_mask = (available_actions == 0).cast(
          "float32"
      )
      agents_q -= 1e8 * unavailable_actions_mask
      actions = paddle.argmax(agents_q, axis=-1).detach().cpu().numpy()
      return actions
  
file_path: PARL/examples/QMIX/qmix_agent.py
imports: import paddle
  import parl
  import numpy
  from utils import AvailableActionsSampler
  import os
label_desc: Use image node_id 6 for calling the QMixAgent obj's underlying member method code with example usage: obj.predict(obs, available_actions) and returns: actions