code: def test_load_connected_checkpoint_default(self):
      prior = KandinskyV22PriorPipeline.from_pretrained(
          "hf-internal-testing/tiny-random-kandinsky-v22-prior"
      )
      decoder = KandinskyV22Pipeline.from_pretrained(
          "hf-internal-testing/tiny-random-kandinsky-v22-decoder"
      )
  
      # check that combined pipeline loads both prior & decoder because of
      # https://huggingface.co/hf-internal-testing/tiny-random-kandinsky-v22-decoder/blob/8baff9897c6be017013e21b5c562e5a381646c7e/README.md?code=true#L3
      assert (
          KandinskyV22CombinedPipeline._load_connected_pipes
      )  # combined pipelines will download more checkpoints that just the one specified
      pipeline = KandinskyV22CombinedPipeline.from_pretrained(
          "hf-internal-testing/tiny-random-kandinsky-v22-decoder"
      )
  
      prior_comps = prior.components
      decoder_comps = decoder.components
      for k, component in pipeline.components.items():
          if k.startswith("prior_"):
              k = k[6:]
              comp = prior_comps[k]
          else:
              comp = decoder_comps[k]
  
          if isinstance(component, torch.nn.Module):
              assert state_dicts_almost_equal(
                  component.state_dict(), comp.state_dict()
              )
          elif hasattr(component, "config"):
              assert dict(component.config) == dict(comp.config)
          else:
              assert component.__class__ == comp.__class__
  
file_path: diffusers/tests/pipelines/test_pipelines_combined.py
imports: import unittest
  import torch
  from huggingface_hub import ModelCard
  from diffusers import DDPMScheduler, DiffusionPipeline, KandinskyV22CombinedPipeline, KandinskyV22Pipeline, KandinskyV22PriorPipeline
  from diffusers.pipelines.pipeline_utils import CONNECTED_PIPES_KEYS
label_desc: Use image node_id 4 for calling the CombinedPipelineFastTest obj's underlying member method code with example usage: obj.test_load_connected_checkpoint_default() without return types