code: def _calculate_metrics(self, data: _DATA) -> _METRICS:
      return norm_metrics(
          p=self.p, data=data, target_spaces=self._target_spaces
      )
  
imports: from __future__ import annotations
  import logging
  from typing import Dict, List, Literal, Tuple, overload
  import torch
  from .tools import _DATA, _METRICS, _MASKS, active_sparse_targets_filter, norm_metrics, fpgm_metrics, generate_sparsity
  from ..base.compressor import Compressor, Pruner
  from ..base.target_space import PruningTargetSpace
  from ..base.wrapper import ModuleWrapper
  from ..utils.evaluator import Evaluator
label_desc: Use image node_id 6 for calling the _NormPruner obj's underlying member method code with example usage: obj._calculate_metrics(data) and returns: norm_metrics