code: def _compute_loss_sensitivity(self):
      sensitivities = []
      parameters = []
      for i, node in enumerate(self.analysis["nodes"]):
          if node["prunable"]:
              sensitivities.append(
                  node["prunable_equation_sensitivity"]
              )
              parameters.append(node["prunable_params"])
  
      loss_analysis = pruning_loss_sens_magnitude(self.model_path)
  
      results_model = loss_analysis.results_model
      results = loss_analysis.results
  
      model = {
          "baseline_measurement_key": (
              str(results_model.baseline_measurement_key)
          ),
          "measurements": {
              str(key): val
              for key, val in results_model.averages.items()
          },
      }
      ops = []
  
      for res in results:
          ops.append(
              {
                  "id": res.id_,
                  "name": res.name,
                  "index": res.index,
                  "baseline_measurement_key": (
                      str(res.baseline_measurement_key)
                  ),
                  "measurements": {
                      str(key): val for key, val in res.averages.items()
                  },
              }
          )
  
      pruning = {"model": model, "ops": ops}
      loss = {}
      loss["baseline"] = {}
      loss["pruning"] = pruning
  
      model = PruningModelEvaluator(
          self.analysis,
          None,
          loss,
      )
      model.eval_baseline(default_pruning_settings().sparsity)
      model.eval_pruning(default_pruning_settings())
  
      self.final_analysis = model.to_dict_values()
  
imports: import json
  import logging
  import os.path
  from pathlib import Path
  from tempfile import TemporaryDirectory
  from typing import Tuple, List, Any, Dict
  import torch
  from sparseml.onnx.optim import ModelAnalyzer, pruning_loss_sens_magnitude
  from sparseml.pytorch.optim import ScheduledModifierManager
  from sparseml.pytorch.sparsification import EpochRangeModifier, GMPruningModifier
  from sparseml.pytorch.utils import ModuleExporter
  from sparsify.blueprints.utils import default_epochs_distribution, PruningModelEvaluator, default_pruning_settings
  from sparsify.schemas import ProjectModelAnalysisSchema
  from torch.nn import CrossEntropyLoss, MSELoss
  from torch.optim import SGD
  from tqdm.auto import tqdm
label_desc: Use image node_id 3 for calling the RecipeBuilder obj's underlying member method code with example usage: obj._compute_loss_sensitivity() without return types