class_docstr: Provides base test running methods for testing an adapter method implementation.
class_docstr_tok: ["Provides","base","test","running","methods","for","testing","an","adapter","method","implementation","."]
code: def run_full_model_load_test(self, adapter_config):
      model1 = self.get_model()
      model1.eval()
  
      name = "dummy"
      model1.add_adapter(name, config=adapter_config)
      with tempfile.TemporaryDirectory() as temp_dir:
          model1.save_pretrained(temp_dir)
  
          model2, loading_info = load_model(
              temp_dir, self.model_class, output_loading_info=True
          )
  
      # check if all weights were loaded
      self.assertEqual(0, len(loading_info["missing_keys"]))
      self.assertEqual(0, len(loading_info["unexpected_keys"]))
  
      # check if adapter was correctly loaded
      self.assertTrue(name in model2.adapters_config)
  
      # check equal output
      input_data = self.get_input_samples(config=model1.config)
      model1.to(torch_device)
      model2.to(torch_device)
      with AdapterSetup(name):
          output1 = model1(**input_data)
          output2 = model2(**input_data)
      self.assertEqual(len(output1), len(output2))
      self.assertTrue(torch.equal(output1[0], output2[0]))
  
imports: import copy
  import os
  import tempfile
  import torch
  import adapters
  from adapters import ADAPTER_MODEL_MAPPING, AdapterSetup, AdapterTrainer, AutoAdapterModel
  from adapters.heads import CausalLMHead
  from adapters.utils import WEIGHTS_NAME
  from adapters.wrappers import load_model
  from transformers import TrainingArguments
  from transformers.testing_utils import require_torch, torch_device
label_desc: Use image node_id 9 for calling the AdapterMethodBaseTestMixin obj's underlying member method code with example usage: obj.run_full_model_load_test(adapter_config) without return types