code: def test_integration_torch_conversation_dialogpt_input_ids(self):
      tokenizer = AutoTokenizer.from_pretrained(
          "microsoft/DialoGPT-small"
      )
      model = AutoModelForCausalLM.from_pretrained(
          "microsoft/DialoGPT-small"
      )
      conversation_agent = ConversationalPipeline(
          model=model, tokenizer=tokenizer
      )
  
      conversation_1 = Conversation("hello")
      inputs = conversation_agent.preprocess(conversation_1)
      self.assertEqual(inputs["input_ids"].tolist(), [[31373, 50256]])
  
      conversation_2 = Conversation(
          "how are you ?",
          past_user_inputs=["hello"],
          generated_responses=["Hi there!"],
      )
      inputs = conversation_agent.preprocess(conversation_2)
      self.assertEqual(
          inputs["input_ids"].tolist(),
          [
              [
                  31373,
                  50256,
                  17250,
                  612,
                  0,
                  50256,
                  4919,
                  389,
                  345,
                  5633,
                  50256,
              ]
          ],
      )
  
file_path: transformers/tests/pipelines/test_pipelines_conversational.py
imports: import gc
  import unittest
  from transformers import MODEL_FOR_CAUSAL_LM_MAPPING, MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING, AutoModelForCausalLM, AutoModelForSeq2SeqLM, AutoTokenizer, BlenderbotSmallForConditionalGeneration, BlenderbotSmallTokenizer, Conversation, ConversationalPipeline, TFAutoModelForCausalLM, pipeline
  from transformers.testing_utils import backend_empty_cache, is_pipeline_test, is_torch_available, require_tf, require_torch, slow, torch_device
  from .test_pipelines_common import ANY
label_desc: Use image node_id 8 for calling the ConversationalPipelineTests obj's underlying member method code with example usage: obj.test_integration_torch_conversation_dialogpt_input_ids() without return types