docstr: is_valid is used in Ensemble to check if it has more than 1 model in it.
  If Ensemble has only 1 model in it, then Ensemble shouldn't be used as best model
docstr_tok: ["is_valid","is","used","in","Ensemble","to","check","if","it","has","more","than","1","model","in","it",".","If","Ensemble","has","only","1","model","in","it",",","then","Ensemble","should","n't","be","used","as","best","model"]
code: def is_valid(self):
      """is_valid is used in Ensemble to check if it has more than 1 model in it.
      If Ensemble has only 1 model in it, then Ensemble shouldn't be used as best model
      """
      return True
  
imports: import copy
  import gc
  import json
  import logging
  import os
  import sys
  import time
  import uuid
  import zipfile
  import numpy
  import pandas
  from supervised.algorithms.factory import AlgorithmFactory
  from supervised.algorithms.registry import BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION, REGRESSION, AlgorithmsRegistry
  from supervised.callbacks.callback_list import CallbackList
  from supervised.exceptions import AutoMLException
  from supervised.preprocessing.exclude_missing_target import ExcludeRowsMissingTarget
  from supervised.preprocessing.preprocessing import Preprocessing
  from supervised.utils.additional_metrics import AdditionalMetrics
  from supervised.utils.config import LOG_LEVEL
  from supervised.utils.metric import Metric
  from supervised.validation.validation_step import ValidationStep
  from supervised.utils.jsonencoder import MLJSONEncoder
  import joblib
  import optuna
  from supervised.tuner.optuna.tuner import OptunaTuner
  from supervised.utils.config import mem
  from supervised.utils.learning_curves import LearningCurves
label_desc: Use image node_id 13 for calling the ModelFramework obj's underlying member method code with example usage: obj.is_valid() and returns: True