code: def test_parallel_inference_with_wrong_number_of_heads(self):
      model = AutoAdapterModel.from_config(self.config())
      model.eval()
  
      model.add_adapter("a")
      model.add_adapter("b")
      self.add_head(model, "a", num_labels=2)
      model.to(torch_device)
  
      inputs = self.get_input_samples(config=model.config)
  
      model.active_adapters = Parallel("a", "b")
      model.active_head = ["a"]
      with self.assertRaises(ValueError):
          model(**inputs)
  
      model.active_head = "a"
      with self.assertRaises(ValueError):
          model(**inputs)
  
imports: import copy
  import random
  import torch
  from adapters import ADAPTER_MODEL_MAPPING, AutoAdapterModel, PrefixTuningConfig, SeqBnConfig, T5AdapterModel
  from adapters.composition import BatchSplit, Parallel
  from adapters.models.bert_generation.adapter_model import BertGenerationAdapterModel
  from transformers import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, Trainer, TrainingArguments
  from transformers.testing_utils import require_torch, torch_device
label_desc: Use image node_id 2 for calling the ParallelAdapterInferenceTestMixin obj's underlying member method code with example usage: obj.test_parallel_inference_with_wrong_number_of_heads() without return types