code: def model_evaluation(
      n_samples: int = None, random_state: int = 42, **kwargs
  ) -> Suite:
      """Suite for evaluating the model's performance over different metrics, segments, error analysis, examining \
         overfitting, comparing to baseline, and more.
  
      Parameters
      ----------
      n_samples : int , default: 1_000_000
          number of samples to use for checks that sample data. If none, use the default n_samples per check.
      random_state : int, default: 42
          random seed for all checks.
      **kwargs : dict
          additional arguments to pass to the checks.
  
      Returns
      -------
      Suite
          A suite for evaluating the model's performance.
  
      Examples
      --------
      >>> from deepchecks.nlp.suites import model_evaluation
      >>> suite = model_evaluation(n_samples=1_000_000)
      >>> result = suite.run()
      >>> result.show()
      """
      args = locals()
      args.pop("kwargs")
      non_none_args = {k: v for k, v in args.items() if v is not None}
      kwargs = {**non_none_args, **kwargs}
  
      return Suite(
          "Model Evaluation Suite",
          PredictionDrift(
              **kwargs
          ).add_condition_drift_score_less_than(),
          TrainTestPerformance(
              **kwargs
          ).add_condition_train_test_relative_degradation_less_than(),
          PropertySegmentsPerformance(
              **kwargs
          ).add_condition_segments_relative_performance_greater_than(),
          MetadataSegmentsPerformance(
              **kwargs
          ).add_condition_segments_relative_performance_greater_than(),
      )
  
imports: from deepchecks.nlp import Suite
  from deepchecks.nlp.checks import ConflictingLabels, FrequentSubstrings, LabelDrift, MetadataSegmentsPerformance, PredictionDrift, PropertyDrift, PropertyLabelCorrelation, PropertySegmentsPerformance, SpecialCharacters, TextDuplicates, TextEmbeddingsDrift, TextPropertyOutliers, TrainTestPerformance, TrainTestSamplesMix, UnderAnnotatedMetaDataSegments, UnderAnnotatedPropertySegments, UnknownTokens
label_desc: Use image node_id 3 for calling a global function with example usage: model_evaluation(n_samples, random_state) and returns: Suite