code: def convert_state_dict(state_dict, mapping):
      r"""
      Simply iterates over the state dict and replaces the patterns in `mapping` with the corresponding values.
  
      Args:
          state_dict (`dict[str, torch.Tensor]`):
              The state dict to convert.
          mapping (`dict[str, str]`):
              The mapping to use for conversion, the mapping should be a dictionary with the following structure:
                  - key: the pattern to replace
                  - value: the pattern to replace with
  
      Returns:
          converted_state_dict (`dict`)
              The converted state dict.
      """
      converted_state_dict = {}
      for k, v in state_dict.items():
          # First, filter out the keys that we always want to replace
          for pattern in KEYS_TO_ALWAYS_REPLACE.keys():
              if pattern in k:
                  new_pattern = KEYS_TO_ALWAYS_REPLACE[pattern]
                  k = k.replace(pattern, new_pattern)
  
          for pattern in mapping.keys():
              if pattern in k:
                  new_pattern = mapping[pattern]
                  k = k.replace(pattern, new_pattern)
                  break
          converted_state_dict[k] = v
      return converted_state_dict
  
imports: import enum
label_desc: Use image node_id 1 for calling a global function with example usage: convert_state_dict(state_dict, mapping) and returns: converted_state_dict