code: def prepare_mask_and_masked_image(
      image, mask, height, width, return_image: bool = False
  ):
      """
      Prepares a pair (image, mask) to be consumed by the Stable Diffusion pipeline. This means that those inputs will be
      converted to ``torch.Tensor`` with shapes ``batch x channels x height x width`` where ``channels`` is ``3`` for the
      ``image`` and ``1`` for the ``mask``.
  
      The ``image`` will be converted to ``torch.float32`` and normalized to be in ``[-1, 1]``. The ``mask`` will be
      binarized (``mask > 0.5``) and cast to ``torch.float32`` too.
  
      Args:
          image (Union[np.array, PIL.Image, torch.Tensor]): The image to inpaint.
              It can be a ``PIL.Image``, or a ``height x width x 3`` ``np.array`` or a ``channels x height x width``
              ``torch.Tensor`` or a ``batch x channels x height x width`` ``torch.Tensor``.
          mask (_type_): The mask to apply to the image, i.e. regions to inpaint.
              It can be a ``PIL.Image``, or a ``height x width`` ``np.array`` or a ``1 x height x width``
              ``torch.Tensor`` or a ``batch x 1 x height x width`` ``torch.Tensor``.
  
  
      Raises:
          ValueError: ``torch.Tensor`` images should be in the ``[-1, 1]`` range. ValueError: ``torch.Tensor`` mask
          should be in the ``[0, 1]`` range. ValueError: ``mask`` and ``image`` should have the same spatial dimensions.
          TypeError: ``mask`` is a ``torch.Tensor`` but ``image`` is not
              (ot the other way around).
  
      Returns:
          tuple[torch.Tensor]: The pair (mask, masked_image) as ``torch.Tensor`` with 4
              dimensions: ``batch x channels x height x width``.
      """
  
      # checkpoint. TOD(Yiyi) - need to clean this up later
      deprecation_message = "The prepare_mask_and_masked_image method is deprecated and will be removed in a future version. Please use VaeImageProcessor.preprocess instead"
      deprecate(
          "prepare_mask_and_masked_image",
          "0.30.0",
          deprecation_message,
      )
      if image is None:
          raise ValueError("`image` input cannot be undefined.")
  
      if mask is None:
          raise ValueError("`mask_image` input cannot be undefined.")
  
      if isinstance(image, torch.Tensor):
          if not isinstance(mask, torch.Tensor):
              mask = mask_pil_to_torch(mask, height, width)
  
          if image.ndim == 3:
              image = image.unsqueeze(0)
  
          # Batch and add channel dim for single mask
          if mask.ndim == 2:
              mask = mask.unsqueeze(0).unsqueeze(0)
  
          # Batch single mask or add channel dim
          if mask.ndim == 3:
              # Single batched mask, no channel dim or single mask not batched but channel dim
              if mask.shape[0] == 1:
                  mask = mask.unsqueeze(0)
  
              # Batched masks no channel dim
              else:
                  mask = mask.unsqueeze(1)
  
          assert (
              image.ndim == 4 and mask.ndim == 4
          ), "Image and Mask must have 4 dimensions"
          # assert image.shape[-2:] == mask.shape[-2:], "Image and Mask must have the same spatial dimensions"
          assert (
              image.shape[0] == mask.shape[0]
          ), "Image and Mask must have the same batch size"
  
          # Check image is in [-1, 1]
          # if image.min() < -1 or image.max() > 1:
          #    raise ValueError("Image should be in [-1, 1] range")
  
          # Check mask is in [0, 1]
          if mask.min() < 0 or mask.max() > 1:
              raise ValueError("Mask should be in [0, 1] range")
  
          # Binarize mask
          mask[mask < 0.5] = 0
          mask[mask >= 0.5] = 1
  
          # Image as float32
          image = image.to(dtype=torch.float32)
      elif isinstance(mask, torch.Tensor):
          raise TypeError(
              f"`mask` is a torch.Tensor but `image` (type: {type(image)} is not"
          )
      else:
          # preprocess image
          if isinstance(image, (PIL.Image.Image, np.ndarray)):
              image = [image]
          if isinstance(image, list) and isinstance(
              image[0], PIL.Image.Image
          ):
              # resize all images w.r.t passed height an width
              image = [
                  i.resize((width, height), resample=PIL.Image.LANCZOS)
                  for i in image
              ]
              image = [
                  np.array(i.convert("RGB"))[None, :] for i in image
              ]
              image = np.concatenate(image, axis=0)
          elif isinstance(image, list) and isinstance(
              image[0], np.ndarray
          ):
              image = np.concatenate(
                  [i[None, :] for i in image], axis=0
              )
  
          image = image.transpose(0, 3, 1, 2)
          image = (
              torch.from_numpy(image).to(dtype=torch.float32) / 127.5
              - 1.0
          )
  
          mask = mask_pil_to_torch(mask, height, width)
          mask[mask < 0.5] = 0
          mask[mask >= 0.5] = 1
  
      if image.shape[1] == 4:
          # images are in latent space and thus can't
          # be masked set masked_image to None
          # we assume that the checkpoint is not an inpainting
          # checkpoint. TOD(Yiyi) - need to clean this up later
          masked_image = None
      else:
          masked_image = image * (mask < 0.5)
  
      # n.b. ensure backwards compatibility as old function does not return image
      if return_image:
          return mask, masked_image, image
  
      return mask, masked_image
  
imports: import inspect
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  import numpy
  import PIL.Image
  import torch
  from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer, CLIPVisionModelWithProjection
  from ...image_processor import PipelineImageInput, VaeImageProcessor
  from ...loaders import FromSingleFileMixin, IPAdapterMixin, StableDiffusionXLLoraLoaderMixin, TextualInversionLoaderMixin
  from ...models import AutoencoderKL, ImageProjection, UNet2DConditionModel
  from ...models.attention_processor import AttnProcessor2_0, FusedAttnProcessor2_0, LoRAAttnProcessor2_0, LoRAXFormersAttnProcessor, XFormersAttnProcessor
  from ...models.lora import adjust_lora_scale_text_encoder
  from ...schedulers import KarrasDiffusionSchedulers
  from ...utils import USE_PEFT_BACKEND, deprecate, is_invisible_watermark_available, is_torch_xla_available, logging, replace_example_docstring, scale_lora_layers, unscale_lora_layers
  from ...utils.torch_utils import randn_tensor
  from ..pipeline_utils import DiffusionPipeline
  from .pipeline_output import StableDiffusionXLPipelineOutput
label_desc: Use image node_id 3 for calling a global function with example usage: prepare_mask_and_masked_image(image, mask, height, width, return_image) and returns: mask,  masked_image, mask,  masked_image,  image