class_docstr: Pipeline for generating image prior for Kandinsky
  
  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:
      prior ([`PriorTransformer`]):
          The canonincal unCLIP prior to approximate the image embedding from the text embedding.
      image_encoder ([`CLIPVisionModelWithProjection`]):
          Frozen image-encoder.
      text_encoder ([`CLIPTextModelWithProjection`]):
          Frozen text-encoder.
      tokenizer (`CLIPTokenizer`):
          Tokenizer of class
          [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer).
      scheduler ([`UnCLIPScheduler`]):
          A scheduler to be used in combination with `prior` to generate image embedding.
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code: def __init__(
      self,
      prior: PriorTransformer,
      image_encoder: CLIPVisionModelWithProjection,
      text_encoder: CLIPTextModelWithProjection,
      tokenizer: CLIPTokenizer,
      scheduler: UnCLIPScheduler,
      image_processor: CLIPImageProcessor,
  ):
      super().__init__()
  
      self.register_modules(
          prior=prior,
          text_encoder=text_encoder,
          tokenizer=tokenizer,
          scheduler=scheduler,
          image_encoder=image_encoder,
          image_processor=image_processor,
      )
  
imports: from typing import List, Optional, Union
  import PIL.Image
  import torch
  from transformers import CLIPImageProcessor, CLIPTextModelWithProjection, CLIPTokenizer, CLIPVisionModelWithProjection
  from ...models import PriorTransformer
  from ...schedulers import UnCLIPScheduler
  from ...utils import logging, replace_example_docstring
  from ...utils.torch_utils import randn_tensor
  from ..kandinsky import KandinskyPriorPipelineOutput
  from ..pipeline_utils import DiffusionPipeline
label_desc: Use image node_id 1 to create a new KandinskyV22PriorEmb2EmbPipeline object from inherited base classes: DiffusionPipeline with example: obj = KandinskyV22PriorEmb2EmbPipeline(prior, image_encoder, text_encoder, tokenizer, scheduler, image_processor)