docstr: Calibrate output probabilities using logits and labels from validation set.
docstr_tok: ["Calibrate","output","probabilities","using","logits","and","labels","from","validation","set","."]
code: def train_calibration(
      self,
      logits: Union[torch.Tensor, np.ndarray],
      labels: Union[torch.Tensor, np.ndarray],
  ) -> CalibrationResult:
      """Calibrate output probabilities using logits and labels from validation set."""
      return NotImplementedError()
  
imports: import logging
  from abc import ABC, abstractmethod
  from dataclasses import dataclass
  from typing import List, Type, Union
  import numpy
  import torch
  import torch.nn
  from ludwig.api_annotations import DeveloperAPI
  from ludwig.constants import BINARY, CATEGORY
  from ludwig.utils.registry import DEFAULT_KEYS, Registry
label_desc: Use image node_id 1 for calling the CalibrationModule obj's underlying member method code with example usage: obj.train_calibration(logits, labels) and returns: NotImplementedError