code: def test_batched_nan_fp16(self):
      # a bug manifested starting at models facebook/opt-1.3 and larger when running batched generations,
      # therefore not using a tiny model, but the smallest model the problem was seen with which is opt-1.3b.
      # please refer to this github thread: https://github.com/huggingface/transformers/pull/17437 for more details
      model_name = "facebook/opt-1.3b"
      tokenizer = GPT2Tokenizer.from_pretrained(
          model_name, use_fast=False, padding_side="left"
      )
  
      model = OPTForCausalLM.from_pretrained(
          model_name, torch_dtype=torch.float16, use_cache=True
      ).cuda()
      model = model.eval()
  
      batch = tokenizer(
          ["Who are you?", "Joe Biden is the president of"],
          padding=True,
          return_tensors="pt",
      )
  
      input_ids = batch["input_ids"].cuda()
      attention_mask = batch["attention_mask"].cuda()
  
      with torch.no_grad():
          outputs = model(input_ids, attention_mask=attention_mask)
          self.assertFalse(torch.isnan(outputs.logits[0]).any().item())
  
imports: import copy
  import tempfile
  import unittest
  import timeout_decorator
  from transformers import OPTConfig, is_torch_available
  from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
  from ...generation.test_generation_utils import GenerationTesterMixin
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, ids_tensor
label_desc: Use image node_id 5 for calling the OPTGenerationTest obj's underlying member method code with example usage: obj.test_batched_nan_fp16() without return types