code: def _metric_fuse(metrics: _METRICS) -> torch.Tensor:
      # mean all metric value
      fused_metric = None
      count = 0
      for _, module_metrics in metrics.items():
          for _, target_metric in module_metrics.items():
              if fused_metric is not None:
                  fused_metric += target_metric
              else:
                  fused_metric = target_metric.clone()
              count += 1
      assert fused_metric is not None
      return fused_metric / count
  
imports: from __future__ import annotations
  from collections import defaultdict
  from functools import reduce
  import heapq
  from typing import Callable, Dict, List, Tuple
  import numpy
  import torch
  from .calculate_metrics import _METRICS
  from ...base.compressor import _PRUNING_TARGET_SPACES
  from ...base.target_space import PruningTargetSpace, TargetType
label_desc: Use image node_id 12 for calling a global function with example usage: _metric_fuse(metrics) and returns: unknown