code: def __init__(
      self,
      vqvae: VQModel,
      tokenizer: CLIPTokenizer,
      text_encoder: CLIPTextModelWithProjection,
      transformer: UVit2DModel,
      scheduler: AmusedScheduler,
  ):
      super().__init__()
  
      self.register_modules(
          vqvae=vqvae,
          tokenizer=tokenizer,
          text_encoder=text_encoder,
          transformer=transformer,
          scheduler=scheduler,
      )
      self.vae_scale_factor = 2 ** (
          len(self.vqvae.config.block_out_channels) - 1
      )
      self.image_processor = VaeImageProcessor(
          vae_scale_factor=self.vae_scale_factor, do_normalize=False
      )
      self.mask_processor = VaeImageProcessor(
          vae_scale_factor=self.vae_scale_factor,
          do_normalize=False,
          do_binarize=True,
          do_convert_grayscale=True,
          do_resize=True,
      )
      self.scheduler.register_to_config(masking_schedule="linear")
  
file_path: diffusers/src/diffusers/pipelines/amused/pipeline_amused_inpaint.py
imports: from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  import torch
  from transformers import CLIPTextModelWithProjection, CLIPTokenizer
  from ...image_processor import PipelineImageInput, VaeImageProcessor
  from ...models import UVit2DModel, VQModel
  from ...schedulers import AmusedScheduler
  from ...utils import replace_example_docstring
  from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
label_desc: Use image node_id 1 to create a new AmusedInpaintPipeline object from inherited base classes: DiffusionPipeline with example: obj = AmusedInpaintPipeline(vqvae, tokenizer, text_encoder, transformer, scheduler)