code: def test_parallel_generate(self):
      if self.config_class not in ADAPTER_MODEL_MAPPING or (
          not hasattr(
              ADAPTER_MODEL_MAPPING[self.config_class],
              "add_seq2seq_lm_head",
          )
          and not hasattr(
              ADAPTER_MODEL_MAPPING[self.config_class],
              "add_causal_lm_head",
          )
      ):
          self.skipTest("No seq2seq or causal language model head")
  
      model1 = AutoAdapterModel.from_config(self.config())
      model1.add_adapter("adapter1")
      model1.add_adapter("adapter2")
      if hasattr(model1, "add_seq2seq_lm_head"):
          model1.add_seq2seq_lm_head("adapter1")
          model1.add_seq2seq_lm_head("adapter2")
      else:
          model1.add_causal_lm_head("adapter1")
          model1.add_causal_lm_head("adapter2")
      model1.set_active_adapters(Parallel("adapter1", "adapter2"))
      model1.to(torch_device)
  
      seq_output_length = 32
  
      # Finally, also check if generation works properly
      input_ids = self.get_input_samples((1, 4), config=model1.config)[
          "input_ids"
      ]
      input_ids = input_ids.to(torch_device)
      generated = model1.generate(
          input_ids, max_length=seq_output_length
      )
      self.assertLessEqual(generated.shape, (2, seq_output_length))
  
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 4 for calling the ParallelAdapterInferenceTestMixin obj's underlying member method code with example usage: obj.test_parallel_generate() without return types