class_docstr: Basic tests on the util functions in TorchAgent.
class_docstr_tok: ["Basic","tests","on","the","util","functions","in","TorchAgent","."]
docstr: Tests batch_respond() in the base Agent class, where the agent provides a batch
  response to a batch of messages.
docstr_tok: ["Tests","batch_respond","(",")","in","the","base","Agent","class",",","where","the","agent","provides","a","batch","response","to","a","batch","of","messages","."]
code: def test_batch_respond(self):
      """
      Tests batch_respond() in the base Agent class, where the agent provides a batch
      response to a batch of messages.
      """
      agent = get_agent()
  
      obs_labs = [
          Message(
              {
                  "text": "It's only a flesh wound.",
                  "labels": ["Yield!"],
                  "episode_done": True,
              }
          ),
          Message(
              {
                  "text": "The needs of the many outweigh...",
                  "labels": ["The needs of the few."],
                  "episode_done": True,
              }
          ),
          Message(
              {
                  "text": "Hello there.",
                  "labels": ["General Kenobi."],
                  "episode_done": True,
              }
          ),
      ]
      response = agent.batch_respond(obs_labs)
      for i, resp in enumerate(response):
          self.assertEqual(resp, "Training {}!".format(i))
  
      obs_elabs = [
          Message(
              {
                  "text": "It's only a flesh wound.",
                  "eval_labels": ["Yield!"],
                  "episode_done": True,
              }
          ),
          Message(
              {
                  "text": "The needs of the many outweigh...",
                  "eval_labels": ["The needs of the few."],
                  "episode_done": True,
              }
          ),
          Message(
              {
                  "text": "Hello there.",
                  "eval_labels": ["General Kenobi."],
                  "episode_done": True,
              }
          ),
      ]
      response = agent.batch_respond(obs_elabs)
      for i, resp in enumerate(response):
          self.assertIn("Evaluating {}".format(i), resp)
  
imports: import os
  import unittest
  from parlai.core.agents import create_agent_from_shared
  from parlai.utils.testing import tempdir
  from parlai.utils.misc import Message
label_desc: Use image node_id 15 for calling the TestTorchAgent obj's underlying member method code with example usage: obj.test_batch_respond() without return types