class_docstr: Grouped KNN density estimator.
  
  Keeps track of the group (e.g. video id) from which the features were sampled for normalization purposes.
  
  Args:
      n_neighbors (int): Number of neighbors used in KNN search.
docstr: Compute min-max normalization statistics while taking the group into account.
code: def _compute_normalization_statistics(self):
      """Compute min-max normalization statistics while taking the group into account."""
      for group, features in self.memory_bank.items():
          distances = self.predict(features, group, normalize=False)
          self.normalization_statistics.update(distances)
  
      self.normalization_statistics.compute()
  
imports: from __future__ import annotations
  from abc import ABC, abstractmethod
  from typing import Any
  import torch
  from sklearn.mixture import GaussianMixture
  from torch import Tensor, nn
  from anomalib.models.ai_vad.features import FeatureType
  from anomalib.utils.metrics.min_max import MinMax
label_desc: Use image node_id 6 for calling the GroupedKNNEstimator obj's underlying member method code with example usage: obj._compute_normalization_statistics() without return types