code: def dummy_prior(self):
      torch.manual_seed(0)
  
      model_kwargs = {
          "num_attention_heads": 2,
          "attention_head_dim": 12,
          "embedding_dim": self.text_embedder_hidden_size,
          "num_layers": 1,
      }
  
      model = PriorTransformer(**model_kwargs)
      # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
      model.clip_std = nn.Parameter(torch.ones(model.clip_std.shape))
      return model
  
imports: import inspect
  import unittest
  import numpy
  import torch
  from torch import nn
  from transformers import CLIPImageProcessor, CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer, CLIPVisionConfig, CLIPVisionModelWithProjection
  from diffusers import KandinskyV22PriorPipeline, PriorTransformer, UnCLIPScheduler
  from diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device
  from ..test_pipelines_common import PipelineTesterMixin
label_desc: Use image node_id 8 for calling the Dummies obj's underlying member method code with example usage: obj.dummy_prior() and returns: model