class_docstr: Pipeline for text-guided image inpainting using Kandinsky2.1
  
  This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the
  library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.)
  
  Args:
      scheduler ([`DDIMScheduler`]):
          A scheduler to be used in combination with `unet` to generate image latents.
      unet ([`UNet2DConditionModel`]):
          Conditional U-Net architecture to denoise the image embedding.
      movq ([`VQModel`]):
          MoVQ Decoder to generate the image from the latents.
code: def guidance_scale(self):
      return self._guidance_scale
  
imports: from copy import deepcopy
  from typing import Callable, Dict, List, Optional, Union
  import numpy
  import PIL.Image
  import torch
  import torch.nn.functional
  from packaging import version
  from PIL import Image
  from ...None import __version__
  from ...models import UNet2DConditionModel, VQModel
  from ...schedulers import DDPMScheduler
  from ...utils import deprecate, logging
  from ...utils.torch_utils import randn_tensor
  from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
label_desc: Use image node_id 3 for calling the KandinskyV22InpaintPipeline obj's underlying member method code with example usage: obj.guidance_scale() and returns: self