code: def _model_std_quality(std: np.ndarray) -> float:
      """Quantifies quality of the model uncertainty. A value of one means the
      uncertainty is perfectly predictive of the true standard deviation of the error.
      Values larger than one indicate over-estimation and negative values indicate
      under-estimation of the true standard deviation of the error. In particular, a value
      of 2 (resp. 1 / 2) represents an over-estimation (resp. under-estimation) of the
      true standard deviation of the error by a factor of 2.
  
      Args:
          std: The standard deviation of the standardized error.
  
      Returns:
          The factor corresponding to the worst over- or under-estimation factor of the
          standard deviation of the error among all experimentally observed metrics.
      """
      max_std, min_std = np.max(std), np.min(std)
      # comparing worst over-estimation factor with worst under-estimation factor
      inv_model_std_quality = (
          max_std if max_std > 1 / min_std else min_std
      )
      # reciprocal so that values greater than one indicate over-estimation and
      # values smaller than indicate underestimation of the uncertainty.
      return 1 / inv_model_std_quality
  
file_path: Ax/ax/telemetry/scheduler.py
imports: from __future__ import annotations
  from dataclasses import asdict, dataclass
  from typing import Any, Dict, Optional
  from warnings import warn
  import numpy
  from ax.service.scheduler import get_fitted_model_bridge, Scheduler
  from ax.telemetry.common import _get_max_transformed_dimensionality
  from ax.telemetry.experiment import ExperimentCompletedRecord, ExperimentCreatedRecord
  from ax.telemetry.generation_strategy import GenerationStrategyCreatedRecord
label_desc: Use image node_id 1 for calling a global function with example usage: _model_std_quality(std) and returns: unknown