class_docstr: This component is used to paginate the results of a retriever.
  It is useful when the retriever returns a large number of documents, and we want to pass them to the LLM
  in batches.
  
  It is useful in cases where the LLM's context length is limited, and we want to avoid passing too many
  documents to it at once.
class_docstr_tok: ["This","component","is","used","to","paginate","the","results","of","a","retriever",".","It","is","useful","when","the","retriever","returns","a","large","number","of","documents",",","and","we","want","to","pass","them","to","the","LLM","in","batches",".","It","is","useful","in","cases","where","the","LLM","'s","context","length","is","limited",",","and","we","want","to","avoid","passing","too","many","documents","to","it","at","once","."]
code: def __init__(
      self, retriever: Any, page_size: int = 1, top_k: int = 100
  ):
      self.retriever = retriever
      self.page_size = page_size
      self.top_k = top_k
      self.retrieved_documents = None
  
file_path: haystack/examples/rag/rag_self_correction.py
imports: from typing import List, Any, Optional, Dict
  import logging
  from pprint import pprint
  from haystack import Pipeline, Document, component, default_to_dict, default_from_dict, DeserializationError
  from haystack.document_stores.in_memory import InMemoryDocumentStore
  from haystack.components.retrievers.in_memory import InMemoryBM25Retriever
  from haystack.components.generators import OpenAIGenerator
  from haystack.components.builders.prompt_builder import PromptBuilder
  from haystack.components.others import Multiplexer
  from haystack.components.routers.conditional_router import ConditionalRouter
  from haystack.core.component.types import Variadic
label_desc: Use image node_id 1 to create a new PaginatedRetriever object  with example: obj = PaginatedRetriever(retriever, page_size, top_k)