class_docstr: Metric for testing factorial designs assuming a main effects only
  logit model.
class_docstr_tok: ["Metric","for","testing","factorial","designs","assuming","a","main","effects","only","logit","model","."]
code: def is_available_while_running(cls) -> bool:
      # This metric does not require a trial to complete to fetch its
      # data, since there is no actual "data" to be fetched –– its
      # fabricated from parameterizations.
      return True
  
file_path: Ax/ax/metrics/factorial.py
imports: from typing import Any, Dict, Tuple
  import numpy
  import pandas
  from ax.core.base_trial import BaseTrial
  from ax.core.batch_trial import BatchTrial
  from ax.core.data import Data
  from ax.core.metric import Metric, MetricFetchE, MetricFetchResult
  from ax.core.types import TParameterization, TParamValue
  from ax.utils.common.result import Err, Ok
  from ax.utils.stats.statstools import agresti_coull_sem
label_desc: Use image node_id 2 for calling the FactorialMetric obj's underlying member method code with example usage: obj.is_available_while_running(cls) and returns: True