class_docstr: Utility class containing a conversation and its history. This class is meant to be used as an input to the
  [`ConversationalPipeline`]. The conversation contains a number of utility function to manage the addition of new
  user input and generated model responses. A conversation needs to contain an unprocessed user input before being
  passed to the [`ConversationalPipeline`]. This user input is either created when the class is instantiated, or by
  calling `conversational_pipeline.append_response("input")` after a conversation turn.
  
  Arguments:
      text (`str`, *optional*):
          The initial user input to start the conversation. If not provided, a user input needs to be provided
          manually using the [`~Conversation.add_user_input`] method before the conversation can begin.
      conversation_id (`uuid.UUID`, *optional*):
          Unique identifier for the conversation. If not provided, a random UUID4 id will be assigned to the
          conversation.
      past_user_inputs (`List[str]`, *optional*):
          Eventual past history of the conversation of the user. You don't need to pass it manually if you use the
          pipeline interactively but if you want to recreate history you need to set both `past_user_inputs` and
          `generated_responses` with equal length lists of strings
      generated_responses (`List[str]`, *optional*):
          Eventual past history of the conversation of the model. You don't need to pass it manually if you use the
          pipeline interactively but if you want to recreate history you need to set both `past_user_inputs` and
          `generated_responses` with equal length lists of strings
  
  Usage:
  
  ```python
  conversation = Conversation("Going to the movies tonight - any suggestions?")
  
  # Steps usually performed by the model when generating a response:
  # 1. Mark the user input as processed (moved to the history)
  conversation.mark_processed()
  # 2. Append a mode response
  conversation.append_response("The Big lebowski.")
  
  conversation.add_user_input("Is it good?")
  ```
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docstr: Iterates over all blobs of the conversation.
  
  Returns: Iterator of (is_user, text_chunk) in chronological order of the conversation. `is_user` is a `bool`,
  `text_chunks` is a `str`.
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code: def iter_texts(self):
      """
      Iterates over all blobs of the conversation.
  
      Returns: Iterator of (is_user, text_chunk) in chronological order of the conversation. `is_user` is a `bool`,
      `text_chunks` is a `str`.
      """
      for user_input, generated_response in zip(
          self.past_user_inputs, self.generated_responses
      ):
          yield True, user_input
          yield False, generated_response
      if self.new_user_input:
          yield True, self.new_user_input
  
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 6 for calling the Conversation obj's underlying member method code with example usage: obj.iter_texts() without return types