code: def _assess_iterations(
      self, X, y, sample_weight, eval_set, max_time=None
  ):
      if max_time is None:
          max_time = 3600
      try:
          model = copy.deepcopy(self.model)
          model.set_params(iterations=self.warmup_iterations)
          start_time = time.time()
          model.fit(
              X,
              y,
              sample_weight=sample_weight,
              cat_features=self.cat_features,
              init_model=None
              if self.model.tree_count_ is None
              else self.model,
              eval_set=eval_set,
              early_stopping_rounds=self.early_stopping_rounds,
              verbose_eval=False,
          )
          elapsed_time = (time.time() - start_time) / float(
              self.warmup_iterations
          )
          # print(max_time, elapsed_time, max_time / elapsed_time, np.round(time.time() - start_time, 2))
          new_rounds = int(min(10000, max_time / elapsed_time))
          new_rounds = max(new_rounds, 10)
          return model, new_rounds
      except Exception as e:
          # print(str(e))
          return None, 1000
  
imports: import copy
  import logging
  import os
  import time
  import numpy
  import pandas
  from sklearn.base import ClassifierMixin, RegressorMixin
  from supervised.algorithms.algorithm import BaseAlgorithm
  from supervised.algorithms.registry import BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION, REGRESSION, AlgorithmsRegistry
  from supervised.preprocessing.preprocessing_utils import PreprocessingUtils
  from supervised.utils.config import LOG_LEVEL
  from supervised.utils.metric import CatBoostEvalMetricAveragePrecision, CatBoostEvalMetricMSE, CatBoostEvalMetricPearson, CatBoostEvalMetricSpearman, CatBoostEvalMetricUserDefined
  import catboost
  from catboost import CatBoost, CatBoostClassifier, CatBoostRegressor, Pool
label_desc: Use image node_id 2 for calling the CatBoostAlgorithm obj's underlying member method code with example usage: obj._assess_iterations(X, y, sample_weight, eval_set, max_time) and returns: model,  new_rounds, None,  int