code: def __init__(
      self,
      tokenizer: T5Tokenizer,
      text_encoder: T5EncoderModel,
      unet: UNet2DConditionModel,
      scheduler: DDPMScheduler,
      safety_checker: Optional[IFSafetyChecker],
      feature_extractor: Optional[CLIPImageProcessor],
      watermarker: Optional[IFWatermarker],
      requires_safety_checker: bool = True,
  ):
      super().__init__()
  
      if safety_checker is None and requires_safety_checker:
          logger.warning(
              f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
              " that you abide to the conditions of the IF license and do not expose unfiltered"
              " results in services or applications open to the public. Both the diffusers team and Hugging Face"
              " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"
              " it only for use-cases that involve analyzing network behavior or auditing its results. For more"
              " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."
          )
  
      if safety_checker is not None and feature_extractor is None:
          raise ValueError(
              "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety"
              " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead."
          )
  
      self.register_modules(
          tokenizer=tokenizer,
          text_encoder=text_encoder,
          unet=unet,
          scheduler=scheduler,
          safety_checker=safety_checker,
          feature_extractor=feature_extractor,
          watermarker=watermarker,
      )
      self.register_to_config(
          requires_safety_checker=requires_safety_checker
      )
  
imports: import html
  import inspect
  import re
  import urllib.parse
  from typing import Any, Callable, Dict, List, Optional, Union
  import numpy
  import PIL.Image
  import torch
  from transformers import CLIPImageProcessor, T5EncoderModel, T5Tokenizer
  from ...loaders import LoraLoaderMixin
  from ...models import UNet2DConditionModel
  from ...schedulers import DDPMScheduler
  from ...utils import BACKENDS_MAPPING, PIL_INTERPOLATION, is_accelerate_available, is_bs4_available, is_ftfy_available, logging, replace_example_docstring
  from ...utils.torch_utils import randn_tensor
  from ..pipeline_utils import DiffusionPipeline
  from .pipeline_output import IFPipelineOutput
  from .safety_checker import IFSafetyChecker
  from .watermark import IFWatermarker
label_desc: Use image node_id 1 to create a new IFInpaintingPipeline object from inherited base classes: DiffusionPipeline, LoraLoaderMixin with example: obj = IFInpaintingPipeline(tokenizer, text_encoder, unet, scheduler, safety_checker, feature_extractor, watermarker, requires_safety_checker)