code: def create_and_check_for_generate_causal_lm(
      self,
      config,
      input_ids,
      token_type_ids,
      input_mask,
      sequence_labels,
      token_labels,
      choice_labels,
  ):
      model = RoFormerForCausalLM(config=config).to(torch_device).eval()
      torch.manual_seed(0)
      output_without_past_cache = model.generate(
          input_ids[:1],
          num_beams=2,
          max_length=15,
          do_sample=True,
          use_cache=False,
      )
      torch.manual_seed(0)
      output_with_past_cache = model.generate(
          input_ids[:1], num_beams=2, max_length=15, do_sample=True
      )
      self.parent.assertTrue(
          torch.all(output_with_past_cache == output_without_past_cache)
      )
  
imports: import unittest
  from transformers import RoFormerConfig, is_torch_available
  from transformers.testing_utils import require_torch, slow, torch_device
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
  from ...test_pipeline_mixin import PipelineTesterMixin
label_desc: Use image node_id 8 for calling the RoFormerModelTester obj's underlying member method code with example usage: obj.create_and_check_for_generate_causal_lm(config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels) without return types