code: def create_default_perturb_fn(key: str):
      def default_perturb_fn(
          data,
          feature_idx,
      ):
          val_data, presence_data = data[key]
          batch_size = val_data.shape[0]
          random_idx = torch.randperm(batch_size)
          val_data[:, feature_idx] = val_data[:, feature_idx][
              random_idx
          ]
          presence_data[:, feature_idx] = presence_data[:, feature_idx][
              random_idx
          ]
          return data
  
      return default_perturb_fn
  
file_path: ReAgent/reagent/evaluation/feature_importance/feature_importance_perturbation.py
imports: import copy
  import logging
  from collections import defaultdict
  from typing import Any, Callable, Optional
  import pandas
  import torch
  import torch.nn
  from reagent.core.dataclasses import dataclass
  from reagent.evaluation.feature_importance.feature_importance_base import FeatureImportanceBase
label_desc: Use image node_id 1 for calling a global function with example usage: create_default_perturb_fn(key) and returns: default_perturb_fn, data