docstr: Checks that the batch of size 2 give twice the same results than the batch of size 1.
docstr_tok: ["Checks","that","the","batch","of","size","2","give","twice","the","same","results","than","the","batch","of","size","1","."]
code: def test_roberta_batching(self, device: str):
      """
      Checks that the batch of size 2 give twice the same results than the batch of size 1.
      """
      _, model = get_toy_model(device)
      sample = mk_sample("en", device, batch_size=1)
      slen = sample["net_input"]["src_lengths"][0]
      sample2 = mk_sample("en", device, batch_size=2)
      with torch.no_grad():
          z = model.encoder.forward(
              sample["net_input"]["src_tokens"],
              sample["net_input"]["src_lengths"],
          )
          z = z["encoder_out"][-1]
          logits, _ = model.forward(**sample["net_input"])
  
          z2 = model.encoder.forward(
              sample2["net_input"]["src_tokens"],
              sample["net_input"]["src_lengths"],
          )
          z2 = z2["encoder_out"][-1]
          logits2, _ = model.forward(**sample2["net_input"])
  
      self.assertEqual(z.shape, (slen, 1, 12))
      self.assertEqual(z2.shape, (slen, 2, 12))
      self.assertTensorEqual(logits2[0], logits2[1])
      self.assertTensorEqual(logits[0], logits2[0])
  
imports: import functools
  import unittest
  from typing import Any, Dict, Sequence
  import fairseq
  import fairseq.options
  import fairseq.tasks
  import torch
  from tests.utils import dummy_dictionary
label_desc: Use image node_id 8 for calling the RobertaTest obj's underlying member method code with example usage: obj.test_roberta_batching(device) without return types