code: def single_iteration(learner_names, model_path):
      for ln in learner_names:
          df = pd.read_csv(
              os.path.join(model_path, f"{ln}_training.log"),
              names=["iteration", "train", "test"],
          )
          if df.shape[0] > 1:
              return False
      return True
  
file_path: mljar-supervised/supervised/utils/learning_curves.py
imports: import logging
  import os
  import numpy
  import pandas
  from supervised.utils.common import learner_name_to_fold_repeat
  from supervised.utils.config import LOG_LEVEL
  from supervised.utils.metric import Metric
  import matplotlib.colors
  import matplotlib.pyplot
label_desc: Use image node_id 1 for calling the LearningCurves obj's underlying member method code with example usage: obj.single_iteration(learner_names, model_path) and returns: True, False