code: def test_inputs_embeds(self):
      (
          config,
          inputs_dict,
      ) = self.model_tester.prepare_config_and_inputs_for_common()
  
      for model_class in (PLBartModel, PLBartForConditionalGeneration):
          model = model_class(config)
          model.to(torch_device)
          model.eval()
  
          inputs = copy.deepcopy(
              self._prepare_for_class(inputs_dict, model_class)
          )
  
          if not self.is_encoder_decoder:
              input_ids = inputs["input_ids"]
              del inputs["input_ids"]
          else:
              encoder_input_ids = inputs["input_ids"]
              decoder_input_ids = inputs.get(
                  "decoder_input_ids", encoder_input_ids
              )
              del inputs["input_ids"]
              inputs.pop("decoder_input_ids", None)
  
          wte = model.get_input_embeddings()
          if not self.is_encoder_decoder:
              inputs["inputs_embeds"] = wte(input_ids)
          else:
              inputs["inputs_embeds"] = wte(encoder_input_ids)
              inputs["decoder_inputs_embeds"] = wte(decoder_input_ids)
  
          with torch.no_grad():
              model(**inputs)[0]
  
imports: import copy
  import tempfile
  import unittest
  from transformers import PLBartConfig, is_torch_available
  from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, require_torch_fp16, slow, torch_device
  from transformers.utils import cached_property
  from ...generation.test_utils import GenerationTesterMixin
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 7 for calling the PLBartModelTest obj's underlying member method code with example usage: obj.test_inputs_embeds() without return types