class_docstr: A class that that supports merging diffusion models based on the discussion here:
  https://github.com/huggingface/diffusers/issues/877
  
  Example usage:-
  
  pipe = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", custom_pipeline="checkpoint_merger.py")
  
  merged_pipe = pipe.merge(["CompVis/stable-diffusion-v1-4","prompthero/openjourney"], interp = 'inv_sigmoid', alpha = 0.8, force = True)
  
  merged_pipe.to('cuda')
  
  prompt = "An astronaut riding a unicycle on Mars"
  
  results = merged_pipe(prompt)
  
  ## For more details, see the docstring for the merge method.
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code: def _remove_meta_keys(self, config_dict: Dict):
      meta_keys = []
      temp_dict = config_dict.copy()
      for key in config_dict.keys():
          if key.startswith("_"):
              temp_dict.pop(key)
              meta_keys.append(key)
      return (temp_dict, meta_keys)
  
file_path: llmga/llmga/diffusers/examples/community/checkpoint_merger.py
imports: import glob
  import os
  from typing import Dict, List, Union
  import safetensors.torch
  import torch
  from huggingface_hub import snapshot_download
  from diffusers import DiffusionPipeline, __version__
  from diffusers.schedulers.scheduling_utils import SCHEDULER_CONFIG_NAME
  from diffusers.utils import CONFIG_NAME, DIFFUSERS_CACHE, ONNX_WEIGHTS_NAME, WEIGHTS_NAME
label_desc: Use image node_id 3 for calling the CheckpointMergerPipeline obj's underlying member method code with example usage: obj._remove_meta_keys(config_dict) and returns: temp_dict,  meta_keys