class_docstr: Metric representing (partial) learning curves of ML model training jobs.
code: def is_available_while_running(cls) -> bool:
      return True
  
imports: from __future__ import annotations
  from abc import ABC, abstractmethod
  from logging import Logger
  from typing import Any, Dict, Iterable, Optional, Set, Union
  import numpy
  import pandas
  from ax.core.base_trial import BaseTrial
  from ax.core.batch_trial import BatchTrial
  from ax.core.experiment import Experiment
  from ax.core.map_data import MapData, MapKeyInfo
  from ax.core.map_metric import MapMetric
  from ax.core.metric import Metric, MetricFetchE, MetricFetchResult
  from ax.core.trial import Trial
  from ax.early_stopping.utils import align_partial_results
  from ax.exceptions.core import UnsupportedError
  from ax.utils.common.logger import get_logger
  from ax.utils.common.result import Err, Ok
  from ax.utils.common.typeutils import checked_cast
label_desc: Use image node_id 2 for calling the AbstractCurveMetric obj's underlying member method code with example usage: obj.is_available_while_running(cls) and returns: True