class_docstr: Test Dyanmic batching when we have a world size fewer than the number of available
  GPUs.
code: def test_tiny_model(self):
      import parlai.scripts.multiprocessing_train as mp_train
  
      from torch.cuda import device_count
  
      if device_count() < 2:
          raise unittest.SkipTest("Need at least 2 GPUs to test")
  
      valid_report, test_report = mp_train.MultiProcessTrain.main(
          model="test_agents/unigram",
          dict_file="zoo:unittest/transformer_generator2/model.dict",
          dict_tokenizer="space",
          task="integration_tests:tiny",
          batchsize=2,
          dynamic_batching="full",
          truncate=8,
          verbose=True,
          validation_every_n_steps=5,
          max_train_steps=10,
      )
  
      assert valid_report["exs"] == 1
      assert test_report["exs"] == 1
  
imports: from parlai.core.opt import Opt
  from parlai.tasks.integration_tests.agents import NUM_TEST, EXAMPLE_SIZE
  from parlai.utils.conversations import Conversations
  import parlai.utils.testing
  import os
  from typing import Dict, Any
  import unittest
label_desc: Use image node_id 1 for calling the TestTinyDataset obj's underlying member method code with example usage: obj.test_tiny_model() without return types