code: def get_key_and_tensorstore_dict(
      layer, checkpoint_info, switch_checkpoint_path
  ):
      if "metadata" in layer:
          split_layer = layer.split("metadata")
          curr_real_layer_name = "".join(split_layer[0])[:-1]
          split_layer = [
              tuple(("metadata" + split_layer[1]).split("/"))
          ]
      elif "kvstore" in layer:
          split_layer = layer.split("kvstore")
          curr_real_layer_name = "".join(split_layer[0])[:-1]
          split_layer = [tuple(("kvstore" + split_layer[1]).split("/"))]
  
      else:
          split_layer = layer.split("/")
          curr_real_layer_name = "/".join(split_layer[:-1])
          split_layer[-1] = (split_layer[-1],)
  
      if "kvstore/path" in layer:
          content = f"{switch_checkpoint_path}/{checkpoint_info[layer]}"
      elif "kvstore/driver" in layer:
          content = "file"
      else:
          content = checkpoint_info[layer]
  
      return curr_real_layer_name, split_layer, content
  
file_path: transformers/src/transformers/models/switch_transformers/convert_big_switch.py
imports: import argparse
  import json
  import os
  import tensorstore
  import torch
  from flax import serialization
  from flax.traverse_util import flatten_dict, unflatten_dict
  from tensorflow.io import gfile
  from transformers.modeling_utils import dtype_byte_size
  from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import rename_keys
  from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
  from transformers.utils.hub import convert_file_size_to_int
label_desc: Use image node_id 2 for calling a global function with example usage: get_key_and_tensorstore_dict(layer, checkpoint_info, switch_checkpoint_path) and returns: curr_real_layer_name,  split_layer,  content