code: def get_model_fitting_status(evaluation_result, metric):
      training_performance = evaluation_result[
          constants.KEY_DATA_TRAIN
      ][metric]
      validation_performance = evaluation_result[
          constants.KEY_DATA_VALID
      ][metric]
  
      if (
          training_performance
          < constants.RECOMMEND_PERFORMANCE_BENCHMARK[metric]
      ):
          return (
              constants.MODEL_STATUS_UNDERFITTING,
              training_performance,
              validation_performance,
          )
  
      if (
          training_performance - validation_performance
          > constants.RECOMMEND_PERFORMANCE_DIFFERENCE_BENCHMARK
      ):
          return (
              constants.MODEL_STATUS_OVERFITTING,
              training_performance,
              validation_performance,
          )
  
      return (
          constants.MODEL_STATUS_FITTING,
          training_performance,
          validation_performance,
      )
  
file_path: contextual-ai/xai/recommendations/recommendation.py
imports: import xai.constants
  from sklearn.metrics.pairwise import cosine_similarity
  import math
label_desc: Use image node_id 1 for calling a global function with example usage: get_model_fitting_status(evaluation_result, metric) and returns: constants,  training_performance,  validation_performance, constants,  training_performance,  validation_performance, constants,  training_performance,  validation_performance