class_docstr: Multi-turn conversational pipeline.
  
  This conversational pipeline can currently be loaded from [`pipeline`] using the following task identifier:
  `"conversational"`.
  
  The models that this pipeline can use are models that have been fine-tuned on a multi-turn conversational task,
  currently: *'microsoft/DialoGPT-small'*, *'microsoft/DialoGPT-medium'*, *'microsoft/DialoGPT-large'*. See the
  up-to-date list of available models on
  [huggingface.co/models](https://huggingface.co/models?filter=conversational).
  
  Usage:
  
  ```python
  conversational_pipeline = pipeline("conversational")
  
  conversation_1 = Conversation("Going to the movies tonight - any suggestions?")
  conversation_2 = Conversation("What's the last book you have read?")
  
  conversational_pipeline([conversation_1, conversation_2])
  
  conversation_1.add_user_input("Is it an action movie?")
  conversation_2.add_user_input("What is the genre of this book?")
  
  conversational_pipeline([conversation_1, conversation_2])
  ```
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code: def __init__(self, *args, **kwargs):
      super().__init__(*args, **kwargs)
      if self.tokenizer.pad_token_id is None:
          self.tokenizer.pad_token = self.tokenizer.eos_token
  
imports: import uuid
  from typing import Any, Dict, List, Optional, Union
  from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
  from .base import PIPELINE_INIT_ARGS, Pipeline
label_desc: Use image node_id 1 to create a new ConversationalPipeline object from inherited base classes: Pipeline with example: obj = ConversationalPipeline()