class_docstr: Base density estimator.
docstr: Update or predict depending on training status.
code: def forward(self, features: dict[FeatureType, Tensor] | Tensor):
      """Update or predict depending on training status."""
      if self.training:
          self.update(features)
          return None
      return self.predict(features)
  
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 4 for calling the BaseDensityEstimator obj's underlying member method code with example usage: obj.forward(features) and returns: self, None