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 trainings_run(self, model, lr=1.0, steps=8):
      # setup dataset
      train_dataset = self.dataset()
  
      training_args = TrainingArguments(
          output_dir="./examples",
          do_train=True,
          learning_rate=lr,
          max_steps=steps,
          no_cuda=True,
          per_device_train_batch_size=2,
          remove_unused_columns=False,
      )
  
      # evaluate
      trainer = AdapterTrainer(
          model=model,
          args=training_args,
          train_dataset=train_dataset,
      )
      trainer.train()
  
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 10 for calling the AdapterMethodBaseTestMixin obj's underlying member method code with example usage: obj.trainings_run(model, lr, steps) without return types