code: def preprocess_mask(mask, batch_size: int = 1):
      if not isinstance(mask, torch.Tensor):
          # preprocess mask
          if isinstance(mask, PIL.Image.Image) or isinstance(
              mask, np.ndarray
          ):
              mask = [mask]
  
          if isinstance(mask, list):
              if isinstance(mask[0], PIL.Image.Image):
                  mask = [
                      np.array(m.convert("L")).astype(np.float32)
                      / 255.0
                      for m in mask
                  ]
              if isinstance(mask[0], np.ndarray):
                  mask = (
                      np.stack(mask, axis=0)
                      if mask[0].ndim < 3
                      else np.concatenate(mask, axis=0)
                  )
                  mask = torch.from_numpy(mask)
              elif isinstance(mask[0], torch.Tensor):
                  mask = (
                      torch.stack(mask, dim=0)
                      if mask[0].ndim < 3
                      else torch.cat(mask, dim=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)
  
      # Check mask shape
      if batch_size > 1:
          if mask.shape[0] == 1:
              mask = torch.cat([mask] * batch_size)
          elif mask.shape[0] > 1 and mask.shape[0] != batch_size:
              raise ValueError(
                  f"`mask_image` with batch size {mask.shape[0]} cannot be broadcasted to batch size {batch_size} "
                  f"inferred by prompt inputs"
              )
  
      if mask.shape[1] != 1:
          raise ValueError(
              f"`mask_image` must have 1 channel, but has {mask.shape[1]} channels"
          )
  
      # Check mask is in [0, 1]
      if mask.min() < 0 or mask.max() > 1:
          raise ValueError("`mask_image` should be in [0, 1] range")
  
      # Binarize mask
      mask[mask < 0.5] = 0
      mask[mask >= 0.5] = 1
  
      return mask
  
imports: import inspect
  from dataclasses import dataclass
  from typing import Any, Callable, Dict, List, Optional, Union
  import numpy
  import PIL.Image
  import torch
  from packaging import version
  from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
  from ...configuration_utils import FrozenDict
  from ...image_processor import VaeImageProcessor
  from ...loaders import LoraLoaderMixin, TextualInversionLoaderMixin
  from ...models import AutoencoderKL, UNet2DConditionModel
  from ...models.lora import adjust_lora_scale_text_encoder
  from ...schedulers import DDIMInverseScheduler, KarrasDiffusionSchedulers
  from ...utils import PIL_INTERPOLATION, USE_PEFT_BACKEND, BaseOutput, deprecate, logging, replace_example_docstring, scale_lora_layers, unscale_lora_layers
  from ...utils.torch_utils import randn_tensor
  from ..pipeline_utils import DiffusionPipeline
  from ..stable_diffusion import StableDiffusionPipelineOutput
  from ..stable_diffusion.safety_checker import StableDiffusionSafetyChecker
label_desc: Use image node_id 4 for calling a global function with example usage: preprocess_mask(mask, batch_size) and returns: mask